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Instructional Guidance Is Key to Promoting Active Learning in Online and Blended Courses Written by: Jay Loftus Ed.D. (MSU / CTLI) & Michele Jacobsen, Ph.D. (Werklund School of Education - University of Calgary)
Abstract - Active learning strategies tend to originate from one of two dominant philosophical perspectives. The first position is active learning as an instructional philosophy, whereby inquiry-based and discovery learning are primary modalities for acquiring new information. The second perspective considers active learning a strategy to supplement the use of more structured forms of instruction, such as direct instruction. From the latter perspective, active learning is employed to reinforce conceptual learning following the presentation of factual or foundational knowledge. This review focuses on the second perspective and uses of active learning as a strategy. We highlight the need and often overlooked requirement for including instructional guidance to ensure active learning, which can be effective and efficient for learning and learners.
Keywords - Active learning, instructional guidance, design strategy, cognitive load, efficiency, online and blended courses
 
Introduction
Learner engagement in online courses has been a central theme in educational research for several years (Martin, Sun and Westing, 2020). As we consider the academic experiences during the COVID-19 pandemic, which began in 2020 and started to subside in 2022, it is essential to reflect on the importance of course quality (Cavanaugh, Jacquemin and Junker, 2023) and learner experience in online courses (Gherghel, Yasuda and Kita, 2023). Rebounding from our collected experience, learner engagement continues to be an important element of course design and delivery. This fact was highlighted in 2021, when the United States Department of Education (DOE) set forth new standards for institutions offering online courses. To be eligible for Title IV funding, new standards require non-correspondence courses to ensure regular and substantive interactions (RSI) between instructors and students (Downs, 2021). This requirement necessitates the need to find ways to engage students allowing instructors the ability to maximize their interactions. One possible solution is to use active learning techniques that have been shown to increase student engagement and learning outcomes (Ashiabi & O’ Neal, 2008; Cavanaugh et al., 2023).
Active learning is an important instructional strategy and pedagogical philosophy used to design quality learning experiences and foster engaging and interactive learning environments. However, this is not a novel perspective. Many years ago in their seminal work, Chickering and Gamson (1987) discussed the issue of interaction between instructors and students, suggesting that this was an essential practice for quality undergraduate education. The newfound focus on active learning strategies has become more pronounced following an examination of instructional practices from 2020 to 2022. For example, Tan, Chng, Chonardo, Ng  and Fung (2020) examined how chemistry instructors incorporated active learning into their instruction to achieve equivalent learning experiences in pre-pandemic classrooms. Similarly, Misra and Mazelfi (2021) described the need to incorporate group work or active learning activities into remote courses to: ‘increase students’ learning motivation, enforce mutual respect for friends’ opinions, foster excitement’ (p. 228). Rincon-Flores & Santos-Guevara (2021) found that gamification as a form of active learning, ‘helped to motivate students to participate actively and improved their academic performance, in a setting where the mode of instruction was remote, synchronous, and online’ (p.43). Further, the implementation of active learning, particularly gamification, was found to be helpful for promoting a more humanizing learning experience (Rincon-Flores & Santos-Guevara, 2021).
This review examines the use of active learning and presents instructional guidance as an often-overlooked element that must be included to make active learning useful and effective. The omission of explicit and direct instructional guidance when using active learning can be inefficient, resulting in an extraneous cognitive burden on learners (Lange, Gorbunova, Shcheglova and Costley, 2022). We hope to outline our justification through a review of active learning and offer strategies to ensure that the implementation of active learning is effective.
Active Learning as an Instructional Philosophy
Active learning is inherently a ‘student-centered’ instructional paradigm that is derived from a constructivist epistemological perspective (Krahenbuhl, 2016; Schunk, 2012). Constructivism theorizes that individuals construct their understanding through interactions and engagements, whereby the refinement of skills and knowledge results over time (Cobb & Bowers, 1999). Through inquiry, students produce experiences and make connections that lead to logical and conceptual growth (Bada & Olusegun, 2015). Engaging learners in activities, tasks, and planned experiences is an overarching premise of active learning as an instructional philosophy. As an overarching instructional philosophy, the role of instructional guidance can be minimized. As Hammer (1997) pointed out many years ago, the role of the instructor in these environments is to provide content and materials, and students are left make ‘discoveries’ through inquiry.
Inquiry-based learning (IBL) is an instructional practice that falls under the general category of ‘active learning’. The tenets of IBL adhere to a constructivist learning philosophy (de Jong et al., 2023) and can be characterized by the following six elements (Duncan & Chinn, 2021). Students will:

Generate knowledge through investigation of a novel issue or problem.
Work ‘actively’ to discover new findings.
Use of evidence to derive conclusions.
Take responsibility for their own learning through ‘epistemological agency’ (Chinn & Iordanou, 2023) and share their learning with a community of learners.
Use problem-solving and reasoning for complex tasks.
Collaborate, share ideas, and derive solutions with peers.

Historically, inquiry-based learning as a form of active learning was adopted as an overall instructional paradigm in disciplines such as medicine and was closely aligned with problem-based learning (PBL) (Barrows, 1996). Proponents of PBL advocate its use because of its emphasis on the development of skills such as communication, collaboration, and critical thinking (Dring, 2019). Critics of these constructivist approaches to instruction highlight the absence of a structure and any form of instructional guidance (Zhang & Cobern, 2021). Instead, they advocate a more explicit form of instruction such as direct instruction (Zhang, Kirschner, Corben and Sweller, 2022).
The view that a hybrid of IBL coupled with direct instruction is the optimal approach to implementing active learning has been highlighted in the recent academic literature (de Jong et al., 2023). The authors suggest that the selection of direct instruction or active learning strategies, such as IBL, should be guided by the desired outcomes of instruction. If the goal of instruction is the acquisition of more foundational or factual information, direct instruction is the preferred strategy. Conversely, IBL strategies are more appropriate ‘for the promotion of deep understanding and transferrable conceptual understanding of topics that are open-ended or susceptible to misconceptions’ (de Jong et al., 2023 p. 7).
The recommendation to use both direct instruction and approaches like IBL has reframed active learning as an instructional strategy rather than an overarching pedagogical philosophy. Active learning should be viewed as a technique or strategy coupled with direct instructional approaches (de Jong et al., 2023).
Active Learning as an Instructional Strategy
Approaching active learning as an instructional strategy rather than an overarching instructional philosophy helps clarify and address the varying perspectives found in the literature. Zhang et al. (2022) suggested that there is a push to emphasize exploration-based pedagogy. This includes instructional approaches deemed to be predicated on inquiry, discovery, or problem-based approaches. This emphasis has resulted in changes to curricular policies that mandate the incorporation of these instructional philosophies. Zhang et al. (2022) discussed how active learning approaches can be incorporated into science education policy to emphasize ‘inquiry’ approaches, despite adequate evidence for effectiveness.  Zhang et al. (2022) stated that the ‘disjoint between policy documents and research evidence is exacerbated by the tendency to ignore categories of research that do not provide the favored research outcomes that support teaching science through inquiry and investigations’ (p. 1162). Instead, Zhang et al. (2022) advocate for direct instruction as the primary mode of instruction in science education with active learning or ‘inquiry’ learning incorporated as a strategy, arguing that conceptual or foundational understanding ‘should not be ‘traded off’ by prioritizing other learning outcomes’ (p. 1172).
In response to Zhang et al. ’s (2022) critique, de Jong et al. (2023) argued that research evidence supports the use of inquiry-based instruction for the acquisition of conceptual understanding in science education. They asserted that both inquiry-based (or active learning approaches) and direct instruction serve specific learning needs. Direct instruction may be superior for foundational or factual learning, while inquiry-based or active learning may be better for conceptual understanding and reinforcement. The conclusion of de Jong et al. ’s (2023) argument suggests the use of a hybrid of direct instruction and active learning techniques, such as inquiry-based designs, depending on the stated learning objectives of the course or the desired outcomes.
This hybrid approach to instructional practice can help ensure that intended learning outcomes are matched with effective instructional strategies. Furthermore, a hybrid approach can help maintain efficiency in learning rather than leaving the acquisition of stated learning outcomes to discovery or happenstance (Slocum & Rolf, 2021).  This notion was supported by Nerantzi's (2020) suggestion that ‘students learn best when they are active and immersed in the learning process, when their curiosity is stimulated, when they can ask questions and debate in and outside the classroom, when they are supported in this process and feel part of a learning community’ (p. 187). Emphasis on learner engagement may support the belief that active learning strategies combined with direct instruction may provide an optimal environment for learning. Active learning strategies can be used to reinforce the direct or explicit presentation of concepts and principles (Lapitan Jr, Tiangco, Sumalinog, Sabarillo and  Diaz, 2021).
Recently, Zhang (2022) examined the importance of integrating direct instruction with hands-on investigation as an instructional model in high school physics classes. Zhang (2022) determined that ‘students benefit more when they develop a thorough theoretical foundation about science ideas before hands-on investigations’ (p. 111). This supports the earlier research in post-secondary STEM disciplines as reported by Freeman, Eddy, McDonough and Wenderoth (2014), where the authors suggested that active learning strategies help to improve student performance. The authors further predicted that active learning interventions would show more significant learning gains when combined with ‘required exercises that are completed outside of formal class sessions’ (p. 8413).
Active Learning Strategies
Active learning is characterized by activities, tasks, and learner interactions. Several characteristics of active learning have been identified, including interaction, peer learning, and instructor presence (Nerantzi, 2020). Technology affords students learning opportunities to connect pre-, during-, and post-formal learning sessions (Zou & Xie, 2019; Nerantzi, 2020). The interactions or techniques that instructors use help determine the types of interactions and outcomes that will result. Instructors may be ‘present’ or active in the process but may not provide adequate instructional guidance for techniques to be efficient or effective (Cooper, Schinske and Tanner, 2021; Kalyuga, Chandler and Sweller. 2001). To highlight this gap, we first consider the widely used technique of think-pair-share, an active learning strategy first introduced by Lyman (1981). This active learning strategy was introduced to provide all students equitable opportunities to think and discuss ideas with their peers. The steps involved in this technique were recently summarized (Cooper et al., 2021): i) provide a prompt or question to students, (ii) give students a chance to think about the question or prompt independently, (iii) have students share their initial answers/responses with a neighbor in a pair or a small group, and (iv) invite a few groups a chance to share their responses with the whole class.
Instructional guidance outlines the structure and actions associated with a task. This includes identifying the goals and subgoals, and suggesting strategies or algorithms to complete the task (Kalyuga et al., 2001). Employing the strategy of think-pair-sharing requires more instructional guidance than instructors may consider. The title of the strategy foreshadows what students will ‘do’ to complete the activity. However, instructional guidance is essential to help students focus on the outcome, rather than merely enacting the process of the activity. Furthermore, instructional guidance or instructions given to students when employing think-pair-sharing can help make this activity more equitable. Cooper et al. (2021) point out that equity is an important consideration when employing think-pair-share. Often, think-pair-share activities are not equitable during the pair or share portion of the exercise, and can be dominated by more vocal or boisterous students. Instructional guidance can help ensure that the activity is more equitable by providing more explicit instructions on expectations for sharing. For example, the instructions for a think-pair-share activity may include those that require each student to compose and then share ideas on a digital whiteboard or on a slide within a larger shared slide deck. The opportunity for equitable learning must be built into the instructions given to students. Otherwise, the learning experience could be meaningless or lack the contribution of students who are timid or find comfort in a passive role during group learning.
Further considerations for instructional guidance are necessary since we now use various forms of Information and Communications Technology (ICT) to promote active learning strategies. Web conferencing tools, such as Zoom, Microsoft Teams, and Google Meet, were used frequently during the height of required remote or hybrid teaching (Ahshan, 2021). Activities that separated students into smaller work groups via breakout rooms or unique discussion threads often included instructions on what students were to accomplish in these smaller collaborative groups. However, the communication of expectations or explicit guidance to help direct students in these groups were often not explicit or were not accessible once the students had been arranged into their isolated workspaces. These active learning exercises would have benefited from clear guidance and instructions on how to ‘call for help’ once separated from the larger group meetings. For example, Li, Xu, He, He, Pribesh, Watson and Major, (2021) described an activity for pair programming that uses zoom breakout rooms. In their description, the authors outlined the steps learners were expected to follow to successfully complete the active learning activity, as well as the mechanisms students used to ask for assistance once isolated from the larger Zoom session that contained the entire class. The description by Li et al. (2021) provided an effective approach to instructional guidance for active learning using Zoom.  Often, instructions are verbalized or difficult to refer to once individuals are removed from the general or common room. The lack of explicit instructional guidance in these activities can result in inefficiency (Kalyuga et al., 2001) and often inequity (Cooper et al., 2021).
The final active learning approach considered here was a case study analysis of asynchronous discussion forums. To extend engagement with course content, students were assigned a case study to discuss in a group discussion forum. The group is invited to apply course concepts and respond to questions as they analyze the case and prepare recommendations and a solution (Hartwell et al., 2021). Findings indicate that case study analysis in discussion forums as an active learning strategy “encouraged collaborative learning and contributed to improvement in cognitive learning” (Seethamraju, 2014, p. 9). While this active learning strategy can engage students with course materials to apply these concepts in new situations, it can also result in a high-volume-low-yield set of responses and posts without sufficient instructional guidance and clear expectations for engagement and deliverables. Hartwell, Anderson, Hanlon, and Brown (2021) offer guidance on the effective use of online discussion forums for case study analysis, such as clear expectations for student work in teams (e.g., a team contract), ongoing teamwork support through regular check-ins and assessment criteria, clear timelines and tasks for individual analysis, combined group discussion and cross-case comparison, review of posted solutions, and requirements for clear connections between case analysis and course concepts.
Active Learning & Cognitive Load Theory
In a recent review of current policy and educational standards within STEM disciplines, Zhang et al. (2022) argued that structured instructional approaches such as direct instruction align more closely with cognitive-based learning theories. These theories are better at predicting learning gains and identifying how learning occurs. Cognitive load theory is one such theory based on three main assumptions. First, humans have the capacity to obtain novel information through problem-solving or from other people. Obtaining information from other individuals is more efficient than generating solutions themselves. Second, acquired information is confronted by an individual’s limited capacity to first store information in working memory and then transfer it to unlimited long-term memory for later use. Problem-solving imposes a heavy burden on limited working memory. Thus, learners often rely on the information obtained from others. Finally, information stored in long-term memory can be transferred back to working memory to deal with familiar situations (Sweller, 2020). The recall of information from long-term memory to working memory is not bound by the limits of the initial acquisition of information in working memory (Zhang et al., 2022).
Zhang et al. (2022) state that ‘there never is a justification for engaging in inquiry-based learning or any other pedagogically identical approaches when students need to acquire complex, novel information’ (p. 1170). This is clearly a one-sided argument that focuses on the acquisition of information rather than the application of acquired information. This also presents an obvious issue related to the efficiency of acquiring novel information. However, Zhang et al. (2022) did not argue against the use of active learning or inquiry learning strategies to help reinforce concepts, or the use of the same to support direct instruction.
The combination of active learning strategies with direct instruction can be modified using assumptions of cognitive load, which highlights the need to include instructional guidance with active learning strategies. The inclusion of clear and precise instructions or instructional guidance is critical for effective active learning strategies (Murphy, 2023). As de Jong et al. (2023) suggest, ‘guidance is (initially) needed to make inquiry learning successful' (p.9). We cannot assume that instructional guidance is implied through the name of the activity or can be determined from the previous learning experiences of students. Assumptions lead to ambiguous learning environments that lack instructional guidance, force learners to infer expectations, and rely on prior and/or potentially limited active learning experiences. In the following section, we offer suggestions for improving the use of active learning strategies in online and blended learning environments by adding instructional guidance.
Suggestions for Improving the Use of Active Learning in Online and Blended Courses
The successful implementation of active learning depends on several factors. One of the most critical barriers to the adoption of active learning is student participation. As Finelli et al. (2018) highlighted, students may be reluctant to participate demonstrating behaviors such as, ‘not participating when asked to engage in an in-class activity, distracting other students, performing the required task with minimal effort, complaining, or giving lower course evaluations’ (p. 81). These behaviors are reminiscent of petulant adolescents, often discouraging instructors from implementing active learning in the future. To overcome this, the authors suggested that providing a clear explanation of the purpose of the active learning exercise would help curb resistance to participation. More recently, de Jong et al. (2023) stated a similar perspective that ‘a key issue in interpreting the impact of inquiry-based instruction is the role of guidance’ (p. 5). The inclusion of clear and explicit steps for completing an active learning exercise is a necessary design strategy. This aspect of instructional guidance is relatively easy to achieve with the arrival of generative artificial intelligence (AI) tools used to support instructors. As Crompton and Burke (2024) pointed out in their recent review, ‘ChatGPT can assist teachers in the creation of content, lesson plans, and learning activities’ (p.384). More specifically, Crompton and Burke (2024) suggested that generative AI could be used to provide step-by-step instructions for students. To illustrate this point, we entered the following prompt into the generative AI tool, goblin.tools (https://goblin.tools/) ‘Provide instructions given to students for a carousel activity in a college class.’ The output is shown in Fig. 1. This tool is used to break down tasks into steps, and if needed, it can further break down each step into a more discrete sequence of steps.

Figure 1 . Goblin.tools instructions for carousel active learning exercises.
The omission of explicit steps or direct instructional guidance in an active learning exercise can potentially increase extraneous cognitive load (Klepsch & Seufert, 2020; Sweller, 2020). This pernicious impact on cognitive load is the result of the diversion of one’s limited capacity to reconcile problems (Zhang, 2022). Furthermore, the complexity of active learning within an online or blended course is exacerbated by the inclusion of technologies used for instructional purposes. Instructional guidance should include requisite guidance for tools used in active learning. Again, generative AI tools, such as goblin.tools, may help mitigate the potential burden on cognitive load. For example, the use of webconferencing tools, such as Zoom or Microsoft Teams, has been pervasive in higher education. Anyone who uses these tools can relate to situations in which larger groups are segmented into smaller groups in isolated breakout rooms. Once participant relocation has occurred, there is often confusion regarding the intended purpose or goals of the breakout room. Newer features, such as collaborative whiteboards, exacerbate confusion and the potential for excessive extraneous load. Generative AI instructions (see Figure 2) could be created and offered to mitigate confusion and cognitive load burden.

Figure 2. Zoom collaborative whiteboard instructions produced by goblin.tools
 
Generative AI has the potential to help outline the steps in active learning exercises. This can be used to minimize confusion and serve as a reference for students. However, instruction alone is often insufficient to make active learning effective. As Finelli et al. (2018) suggest, the inclusion of a rationale for implementing active learning is an effective mechanism to encourage student participation. To this end, we suggest the adoption of what  Bereiter (2014) called Principled Practical Knowledge (PPK) which consists of the combination of ‘know-how’ with ‘know why’ (Bereiter, 2014). This perspective develops out of learners’ efforts to solve practical problems. It is a combination of knowledge that extends beyond simply addressing the task at hand. There is an investment of effort to provide a rationale or justification to address the ‘know why’ portion of PPK (Bereiter, 2014). Creating conditions for learners to develop ‘know-how’ is critical when incorporating active learning strategies in online and blended courses. Instructional guidance can reduce ambiguity and extraneous load and can also increase efficiency and potentially equity.
What is typically not included in the instructional guidance offered to students is comprehensive knowledge that outlines the requirements for technology that is often employed in active learning strategies. Ahshan (2021) suggests that technology skill competency is essential for the instructors and learners to implement the activities smoothly. Therefore, knowledge should include the tools employed in active learning. Instructors cannot assume that learners have a universal baseline of technological competency and thus need to be aware of this diversity when providing instructional guidance.
An often-overlooked element of instructional guidance connected to PPK is the ‘know-why’ component. Learners are often prescribed learning tasks without a rationale or justification for their utility. The underlying assumption for implementing active learning strategies is the benefits of collaboration, communication, and collective problem-solving are clear to learners (Dring, 2019; Hartikainen et al., 2019). However, these perceived benefits or rationales are often not provided explicitly to learners; instead, they are implied through use.
When implementing active learning techniques or strategies in a blended or online course one needs to consider not only the ‘know-how,’ but also the ‘know-why.’ Table 1 helps to identify the scope of instructional guidance that should be provided to students.
 
Table 1. Recommended Type of Instructional Guidance for Active Learning




 


Know How


Know Why




Activity


Steps


Purpose / Rationale




Technology


Steps


Purpose / Rationale




Outcomes / Products


Completion


Goals




 
The purpose of providing clear and explicit instructional guidance to learners is to ensure efficiency, equity, and value in incorporating active learning strategies into online and blended learning environments. Along with our argument for “know-why” (Bereiter, 2012), we draw upon Murphy (2023) who highlights the importance of “know-how’ by stating, ‘if students do not understand how a particular learning design helps them arrive at a particular outcome, they tend to be less invested in a course’ (n.p.).
Clear instructional guidance does not diminish the authenticity of various active learning strategies such as problem-based or inquiry-based techniques. In contrast, guidance serves to scaffold the activity and clearly outline learner expectations. Design standards organizations, such as Quality Matters, suggest the inclusion of statements that indicate a plan for how instructors will engage with learners, as well as the requirements for learner engagement in active learning. These statements regarding instructor engagement could be extended to include more transparency in the selection of instructional strategies. Murphy (2023) suggested that instructors should ‘pull back the curtain’ and take a few minutes to share the rationale and research that informs their decision to use strategies such as active learning. Opening a dialogue about the design process with students helps to manage expectations and anxieties that students might have in relation to the ‘What?’, ‘Why?’ and ‘How?’ for the active learning exercises.
Implications for Future Research
We contend that a blend of direct instruction and active learning strategies is optimized by instructional guidance, which provides explicit know-how and know-why for students to engage in learning tasks and activities. The present discussion does not intend to evaluate the utility of active learning as an instructional strategy. The efficacy of active learning is a recurring theme in the academic literature, and the justification for efficacy is largely anecdotal or based on self-reporting data from students (Hartikainen, Rintala, Pylväs and Nokelainen, 2019). Regardless, the process of incorporating active learning strategies with direct instruction appears to be beneficial for learning (Ahshan, 2021; Christie & De Graaff, 2017; Mintzes, 2020), and more likely, the learning experience can be harder to quantify. Our argument relates to the necessary inclusion of instructions and guidance that make the goals of active learning more efficient and effective (de Jong et al., 2023). Scardamalia and Bereiter (2006) stated earlier that knowledge about dominates traditional educational practice. It is the stuff of textbooks, curriculum guidelines, subject-matter tests, and typical school “projects” and “research” papers. Knowledge would be the product of active learning. In contrast, knowledge of, ‘suffers massive neglect’ (p. 101).  Knowledge enables learners to do something and allows them to actively participate in an activity. Knowledge comprises both procedural and declarative knowledge.  It is activated when the need for it is encountered in the action. Instructional guidance can help facilitate knowledge of, making the use of active learning techniques more efficient and effective.
Research is needed on the impact of instructional guidance on active learning strategies, especially when considering the incorporation of more sophisticated technologies and authentic problems (Rapanta, Botturi, Goodyear, Guardia and Koole 2021; Varvara, Bernardi, Bianchi, Sinjari and Piattelli, 2021). Recently, Lee (2020) examined the impact of instructor engagement on learning outcomes in an online course and determined that increased instructor engagement correlated with enhanced discussion board posts and student performance. A similar examination of the relationship between the instructional guidance provided and student learning outcomes would be a valuable next step. It could offer more explicit guidance and recommendations for the design and use of active learning strategies in online or blended courses.
Conclusion
Education was disrupted out of necessity for at least two years. This experience forced us to examine our practices in online and blended learning, as our sample size for evaluation grew dramatically. The outcome of our analysis is that effective design and inclusion of student engagement and interactions with instructors are critical for quality learning experiences (Rapanta et al., 2021; Sutarto, Sari and Fathurrochman, 2020; Varvara et al., 2021). Active learning appeals to many students (Christie & De Graaff, 2017) and instructors as it can help achieve many of the desired and required outcomes of our courses and programs. Our review and discussion highlighted the need to provide clear and explicit guidance to help minimize cognitive load and guide students through an invaluable learning experience. Further, instructors and designers who include explicit guidance participate in a metacognitive process, while they outline the purpose and sequence of steps required for the completion of active learning exercises. Creating instructions and providing a rationale for the use of active learning in a course gives instructors and designers an opportunity to reflect on the process and ensure that it aligns with the intended purpose or stated goals of the course. This reflective act makes active learning more intentional in use rather than employing it to ensure that students are present within the learning space.
 
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Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L., & Koole, M. (2021). Balancing technology, pedagogy and the new normal: Post-pandemic challenges for higher education. Postdigital Science and Education, 3(3), 715–742.
Rincon-Flores, E. G., & Santos-Guevara, B. N. (2021). Gamification during Covid-19: Promoting active learning and motivation in higher education. Australasian Journal of Educational Technology, 37(5), 43–60. https://doi.org/10.14742/ajet.7157
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Undergraduate Learning Assistants (ULAs) are an important part of MSU’s educational approach, creating instructional teams by working alongside instructors, course coordinators, and graduate students. To better prepare ULAs for the classroom, the following trainings have been developed. Ideally, ULAs should have completed these at the beginning of their employment. If you are a faculty/staff member with an Undergraduate Learning Assistant (ULA), please forward the following training opportunities to your student assistants. 


Asynchronous Online ULA Training
Students can self-enroll here for a ULA training course covering how the ULA role intersects with the following concepts :

Code of Teaching Responsibility
Student records and privacy
Disability and Accommodation
Creating Inclusive environments
Navigating relationships and the institution

In addition to this asynchronous training, ULAs should have a special RVSM training (details below), and possibly a laboratory safety training if assigned by their course instructor/coordinator (also below).
Relationship Violence Sexual Misconduct (RVSM) for ULAs
In addition to the above course, ULAs should attend one of the following RVSM trainings.
ULA RVSM Training AMonday, Jan 8th, 20242pm-3pmRegister Here
ULA RVSM Training BWednesday, Jan 10th, 202410am-11amRegister Here
Laboratory Safety Training (only required for ULAs in laboratory settings)
If you are a learning assistant in a laboratory, you may need to attend a 1-hour training with Colin Phillipo from Environment Health & Safety. Check with your course instructor/coordinator to see if you need to attend.
The training is 

Friday, January 5th from 10-11am – Register Here

If you are not able to make this training, you can contact Colin Phillippo at phill394@msu.edu to see what accommodations can be made.Photo by fran innocenti on Unsplash

Title
Sr. Instructional Technologist/ Instructional DesignerMSU IT - Instructional Technology and Development Team
Education

Master of Arts in Teaching (MAT), Mathematics Department, Michigan State University
Bachelor of Applied Science (BASc), Mathematics Department, Michigan State University
Michigan Virtual University Master Trainer online certification

Work Experience
I have a teaching background in mathematics and have always been interested in using educational technology to enhance teaching and learning. Shortly after teaching online courses, I took a part-time position at the Center for Teaching Excellence at Lansing Community College to support others with their online classroom design and technology needs. During this time, I accumulated certifications for online teaching and co-facilitated the LCC Teaching Online Certification course. I became an expert in each learning management system that was used through the years (Blackboard, ANGEL, and D2L) and facilitated workshops. I took on a full-time Instructional Designer role at MSU in May 2014, helping with the transition from ANGEL to D2L. I currently serve in working groups that include MSU Learning Systems and University Services (LSUS), and the D2L Technical Account Manager (TAM) biweekly meetings. I also serve as one of the Quality Matters Coordinators at MSU. I developed and maintain several training sites and communities, including the Student D2L Training course, the Instructor D2L Self-directed training, the QM at MSU Community, and the HTML Content Templates site, among others. With the help of the MSU Social Work department, we designed a full course model (course design template) that has been updated by our MSU ITDev team to include professional looking visuals and interactives, as well as student-facing course resources to give instructors a quick start when developing online courses from scratch.
Professional Interests
I enjoy consulting with instructors and peers on LMS features and other edtech tools. For several years now, I have led the monthly D2L Interest Group for Instructional Needs (DIG-IN) - for IDs, trainers, and experienced online instructors with the intent to “empower broadly” and enrich the MSU landscape with D2L experts across departments and programs.
Links to Useful Resources/Articles
Feel free to contact me, halicks@msu.edu, with questions or comments about the following resources and let me know if there is another topic you are interested in learning more about.

D2L Training Courses Flyer (PDF)
MSU IT Course Design Models (Mediaspace)
Quality Matters (QM) at MSU
D2L New Content Experience (Lessons) FAQ
D2L Semester Start Checklist
D2L Course Cleanup
Creating Awards in D2L
D2L Grades at MSU
Getting Started with the Quick Discussion Grader in D2L

Workshop Recordings

Consistent Course Design Matters - Start with a Ready-Made Course Template
IT Virtual Workshop - D2L New Content Experience (09.16.2021)
IT Virtual Workshop - D2L Build-a-Workshop (08.04.2021)
IT Virtual Workshop - Monitoring Your D2L Course
IT Virtual Workshop - D2L Gradebook

The following is a list of the educators receiving the #iteachmsu Educator Award from the College of Education. For more information on these awards, check out the article entitled "#iteachmsu Educator Awards".
Alexandra Lee: Alexandra’s research focuses on the social-psychological processes underpinning achievement motivation specifically implicit theories of intelligence, competence beliefs, and achievement goals. She has taught in the K-12 setting and in a variety of cultural contexts, prior to coming to MSU (in Thailand, Singapore, rural Mississippi, and Denver, CO). As is currently the instructor of record for TE 150 (Reflections on Learning). Alexandra shared her teaching expertise and enthusiasm at a recent Lunch and Learn session with Graduate Teaching Assistants at MSU. Those in attendance really enjoyed learning from her and her expertise. We hope to have Alexandra share more of her great work in the teaching space for all those interested in teaching.
 
Lori Bruner: For always having my back! For being patient and flexible when I was diagnosed with arthritis. For teaching me how to be organized and new technology tricks. For being a great leader and mentor to other graduate and undergraduate students! Thank you for being there and helping me with teaching tasks when I needed it the most!
 
Eliana Castro: In addition to being an incredible, brilliant scholar and a devoted, compassionate teacher educator, Eliana Castro is a generous, contributing citizen in our Department. She provides invaluable service in myriad ways: mentoring other doctoral students, serving on a search committee for a new social studies colleague, helping to recruit incoming doctoral students, among many other ways. She is also a warm, kind person of whom we are all so proud. Thank you, Eliana!
 
Marilyn Amey: I cannot say enough positive things about Dr. Amey. After taking one of her doctoral courses as a part of my PhD electives, I asked Marilyn to be the chair of my dissertation committee (and while the HALE department is filled with talented educators - I feel this was one of the best decisions of my doctoral career). Not only is Marilyn competent in her field and extremely knowledgeable, she is a fantastic educator who creates spaces where learning happens in multiple directions. She is kind, compassionate, and thoughtful - all things she demonstrates as my committee advisor and as a departmental leader. There are few people who have recognized and accommodated me as a "whole person" (with things in my life outside of school), and I will feel forever indebted to her for that. MSU needs more faculty, administrators, and educators like Dr. Marilyn Amey!
 
Courtney Kosloski: Courtney truly has "the backs" of the graduate students in the HALE Department. Every time I interact with her she is professional and kind in helping answer questions and connect me with relevant resources. She keeps students' best interests in mind and takes it upon herself to reach out when better supports can be accessed. She's a wonderful person, and an asset to HALE and MSU. 
 
Mallory Weiner: Mallory is my co-instructor in ANR 310.  This class is unique in that our students come in with a blank syllabus and they create one from scratch, deciding what they want to learn, how they want to learn, and how they want to assess their learning.  Mallory has been instrumental in supporting the learning of our students by preparing them to become self-directed learners with the capacity to practice democratic decision making.  It isn't easy for a student who is a peer to the students in her class to take on the responsibility of instructor, but Mallory wears the crown with ease.  She is an excellent communicator, the ideal partner who doesn't hesitate to take action when necessary, and a supportive co-learner.  I'm proud to know that she will be educating the next generation of learners in K12 classrooms beginning next year.
 
Austin Wellette-Hunsucker: Austin regularly goes above and beyond his duties as a graduate teaching assistant. Not only does he provide tremendous support to the instructor, but he is always willing to go the extra mile for the students. I appreciate his time and effort with the students and the course, and am thankful for his assistance this semester.
 
Taren Going: Taren worked with me as a TA for my TE 407 course. As a 5 credit course, the work is demanding - there are 5 hours of lab per week and 3 hours of seminar. Taren showed tireless dedication to students' success in the course. Her primary responsibilities were to support students' work in their labs, but she often attended seminar to help her understand the core ideas of TE 407 and support students' learning from the lab. She also regularly sought feedback on her performance as a TA because of her genuine concern for students' learning. I could trust Taren to seek help when she needed it. Taren inspires others to work hard and be their best, and I am so grateful she gave so much of her talents and energy to this course.
 
Juan Mascorro-Guerrero: I appreciate Juan because he is our graduate advisor for culturas de las razas unidas outside of helping us with our roles on e board he always offers to help us with applications for scholarships, finding internships, or just provides us a space to talk. Juan is an assistant community director in Wilson Hall , he has a busy schedule but never fails to provide support and help those around him. Juan is the true definition of Latino/Latinx Excellence.
 
Terry Edwards: Terry Edwards is the rock that anchors the TE department. I thanked her last year but that is not enough, not nearly enough for all that she does in the department. Over this semester, Terry has helped me and several other doctoral students in numerous ways. She is always making sure that the doctoral students are thriving--physically and mentally. She ensures to talk to everyone and showers us with affirmations, love, and praise. I am grateful for Terry and also recognize that she does a lot of the emotional and physical work of supporting doctoral students. Terry's commitment is not merely about her role but a much deeper commitment, a commitment that is about creating a space that is welcoming to all and one where everyone feels seen and heard. She has helped me track down packages that are lost. She brought a sewing machine off craigslist because some of us wanted to use it for our work. She plays a crucial role in organizing a department potluck. She is always advocating for us. And no matter what issue you are facing, Terry will do her best to help you find a solution. We are so grateful for Terry and everything she does for us. 
 
Olivia Furman: I (Naseeb) entered into community with Olivia through WOCI, which she co-leads. Last Fall, Olivia worked with an MSU alumnus, Shakara Tyler, to promote a nature centered self-care program, where folx were able to engage in forest walks, soil meditation, and herbal foraging. As a first-year Ph.D. student, I have tremendously valued Olivia’s commitment to addressing the isolation graduate students often experience through holistic wellness practices. Most notably, Olivia has modeled for me how to leverage research to support the wellness of communities our inquiry is based upon. For example, I had the opportunity to engage in an educational research methods course with Olivia this past fall. Despite the overwhelming valuation of quantitative methods in educational inquiry, Olivia drew upon bell hooks, Audre Lorde, the Combahee River Collective and other Black womxn feminisms, as well as her professional experiences with K-12 teaching, to weave together arts-based research methods with Black feminist epistemologies to explore how Black girls experience schooling. She was met with subtle, and sometimes direct, resistance from the course peers who failed to see the transformative and community-based nature of her methodological position. Despite this, she actively pursued her arts-based research agenda, refusing to spend time justifying her methodological decisions and instead carried out her efforts with integrity and creativity. As a non-Black WOC, I have valued Olivia’s leadership example in leveraging the critical practices of wellness found in Black and Brown communities (e.g. quilts, knitting, and ceramics) to reform teaching and learning practices at MSU.
 
Kristi Lowrie: Kristi is an integral part of the TE department and has supported doctoral students tirelessly! She has been pivotal in me having a successful semester. Kristi is always willing to help and goes above and beyond to find resources/solutions. Several times when I walked into her office with a challenge/issue she would drop everything else and help me figure things out. I appreciate Kristi and her relentless support for doctoral students in the program. Thank you, Kristi! 
 
Sheila Orr: In her first year, Sheila has contributed extensively not only to the improvement of secondary mathematics methods courses in teacher education but also to the success of my NSF UTEMPT project. In only a few short months "on the job," Sheila conducted independent analyses of new data for the project and took the lead in presenting this data at a national conference in Pheonix in February. She also went beyond in her role to shadow TE 407, the first mathematics methods course for prospective secondary mathematics teachers (PSTs), by taking the lead in several sessions to help PSTs try out new mathematics teaching practices. I continue to be impressed not only by her passion and drive for learning to teach future teachers, but also by her engagement with improving MSU's coursework, in concert with innovations supported by the UTEMPT project, to better support PSTs' learning.
 
Teacher Education Undergraduate Staff: The undergraduate students working for the TE department are integral to the success of doctoral students. We are deeply appreciative of their hardwork and support. They are always willing to support us with crucial tasks--supplies, scans, photocopies, etc. Even though several of the students are not in the TE program, they go above and beyond to understand the needs of doctoral students and willing to support us. I have also learned a great deal from each of them about their respective fields and appreciate how they brighten up the department with their indomitable spirit! Thank you all for your work. 
 
Dr. Amey’s HALE Graduate Students: Students with whom I work most closely are all adult learners with very complex lives during these difficult times. They are eldercare providers, researchers whose studies have been totally interrupted as they neared completion of dissertations, those hoping for employment next year on and off campus now on hold due to hiring chills and freezes, those who have to find ways to focus on class while becoming homeschool teachers, and those who have put up my constantly shifting schedule of an academic administrator. Yet, they continue to show up to meet with me on zoom and email, inspire through their insights and leadership in these challenging times, find ways to bolster each other in virtual writing groups, and make it clear that postsecondary education will be in good hands. They remind me why I wanted to be a faculty member and are my motivation every day. Thank you isn't enough to each of them.
Anyone can recognize a fellow Spartan for their contributions to MSU's teaching and learning mission or for how they made a lasting impression on your experience. All you have to do is click "Thank an Educator" in the left panel of iteach.msu.edu. From there you'll be directed to a form where you can enter the name, netID, and a short story of the educator you'd like to recognize.

What is iClicker? 
iClicker is a student response system that allows instructors to incorporate interactive and engaging aspects of teaching and learning into their lessons. Students can participate using actual clicker remotes, or their own devices (such as phones, tablets, and laptops) via the iClicker Reef application. 
 
What is the difference between iClicker Cloud and iClicker Reef? 
iClicker Cloud is the portion of the tool that allows for instructors to set up courses and assessments. ICloud Reef is for student responses and can be accessed via different devices as long as students have internet access.  
 
Why is iClicker important?  
With iClicker, instructors can engage students throughout instruction, whether face-to-face or remote. As instructors have the opportunity to incorporate activities like polls and quizzes directly into their lessons, iClicker can be used for both formative and summative assessment. This tool also allows assessments to be graded and transferred to learning management systems such as D2L. 
 
How do I access iClicker? 
iClicker is FREE for all MSU students and instructors. The application can be downloaded here.  
 
Where can I find more information about iClicker? 
The Academic Service Technology Catalog has guidance for instructors and students on iClicker. 
 
 

Although there is no simple solution to responding to a tragedy, the ways that we interact with our students in the next few weeks will be essential to rebuilding our community and moving forward.  Below is a compilation of resources to employ as you return to the classroom.  It is important to attend to the social and emotional needs of both you and your students after this campus tragedy.  Once classes resume, it is important to recognize that while some students will be relieved for the return to a schedule, others may struggle to get back into the academic routine.  Some students are not sleeping, some are experiencing extreme grief and anxiety, and many will have a hard time focusing on academics. Everyone will experience the campus tragedy differently, and it will be important to be flexible as the university moves forward. Below are recommendations for how to engage in “better” practices during this time. 
Move slowly 
You will need to acknowledge that learning will be more challenging since students and instructors have experienced a traumatic event: 

Adjust your weekly in-class activities to be a quarter to a half of what they were.  
Make more space for processing and reflecting on course content both in class and in the time between assignments, readings, etc.  
Consider ending class early each week if students are tired mentally and emotionally. 
Give more short breaks in long classes (consider two short breaks on the hour, vs. one longer break in the middle). 

Resource for Course Policy Modifications After a Crisis Practical Strategies for Returning to Class. This resource contains examples of policies and adaptations you may consider. These are not meant to be read as recommendations that every instructor should adopt, but rather as possibilities that instructors can individually assess and adapt to their own teaching context. 
Focus on Mastery Learning 
A focus on mastery learning can help with an academic disruption. It is important to be mindful of the cognitive load. The goal of mastery learning is to ensure students learn content, not just perform on assessments. Mastery learning offers students multiple opportunities to demonstrate what they know. This is especially helpful when students’ cognition is overwhelmed by trauma. Ideas for implementation include:  

Offer re-takes, re-writes and general revision of work 
Offer a variety of formative and summative assessments including performance-based, written response, or oral communication  
Offer students the opportunity to review  
Organize student review sessions throughout the rest of the semester 
Scaffold prior learning and continue to explicitly build content—any review is helpful 

Resources for mastery learning (K12 resources are relevant) 

What is Mastery Learning. https://research.com/education/what-is-mastery-learning A brief overview of the key elements of mastery learning. 
Mastery Learning. https://tea.dtei.uci.edu/resources/mastery-learning/ A brief overview of mastery learning in the STEM field. 
Grade Expectations. https://www.gse.harvard.edu/news/ed/19/05/grade-expectations. A brief overview of alternatives to high stakes grading. 

Less is more 
Adjust your activities and assignments to potentially be a quarter to a half of what they were. This requires that you focus on the absolute core content of the course. While it is interesting to extend learning around a topic, this is a time to get down to the basics of the content. These prompts might be able to help you make revisions to your syllabus and teaching practices:  

Are there plans that no longer seem realistic?  
Are there activities that you as an instructor do not have the capacity to assess?  
Are there assignments you can take out all together?  
Are there readings that can be on a “to-read” list after the semester rather than required for each week?  
Can students meet in synchronous discussion groups in lieu of writing a discussion board? 

If you’ve responded yes to any of these questions, adjust your syllabus and notify your students. 
Managing evaluation 
After an academic disruption due to a crisis, it can be challenging to adapt your semester plan. Students (and you) will likely have limited cognitive capacity and will need flexibility in learning and assessments. While you should still have high expectations for students, you may need to revise your pedagogy and curriculum. It will be important to consider your curriculum and ask yourself: 

Can some units be combined? 
Are there extended learning elements that can be taken out to focus on core concepts? 
Can assessments (quizzes, tests) be revised slightly to focus on core concepts? 
Instead of a lecture, can you create student focused, small group activities 
What are other ways students can demonstrate knowledge: voice memos and voice to text, mind map, projects (Zines, podcasts, artwork, presentations, etc.)? 

If you’ve responded yes to any of these questions, adjust your syllabus and notify your students. 
Student Autonomy 
One way to support students who experience trauma is to ensure that students have choices about how to manage their own behavior. While there are tasks students need to accomplish to earn a grade, to learn content, and move forward, they do need some cognitive flexibility. At the same time, some students will need direction with firm deadlines. Here are some recommendations for supporting student success: 

Give students an option for when they take exams (day, time, etc.) 
Offer options for the order of the work when able 
Offer deadline flexibility/negotiation for those who need it, and firm deadlines for those who need the structure 
Offer written and verbal options 

Responding to Student Experience 
When classes resume, it is important to acknowledge the campus tragedy with students. You do not need to be a licensed counselor to pause and explicitly state that you recognize the community has been harmed and that you are able to direct students to university resources meant to help them. It is also imperative to recognize our BIPOC, LGBTQIA2S+ , and international student population may experience this trauma differently, as the threat of violence connects directly to their social identity experiences. You can say: 

I recognize that we have been through a tragedy as a campus 
This is a hard time for everyone, and I am happy to listen, and there will be no easy fix 
Please know that there are many resources available to you 
I am happy to refer you to resources that support you during this time 
We will all get through this challenging time together 

Resources with ideas of how you can respond 

https://cft.vanderbilt.edu/guides-sub-pages/crisis/ 
https://ctl.wustl.edu/resources/strategies-for-supporting-students-through-tragedy/ 
Students’ Perceptions of Helpful Faculty Actions Following a Collective Tragedy. This article investigates the most common instructor responses following a tragedy and which of those responses students find most helpful. 
What to say 

Leading Class Discussions  
Acknowledging the collective experience after a campus tragedy is essential. This is why it is important for everyone to respond to the student experience, as described on the first day back resource. You do not have to lead a classroom discussion about the events. For some students, having conversations about the crisis makes them feel less safe. If you do choose to have a discussion, it will be important to inform the class ahead of time that you will be giving time in class to discuss and give students the option to arrive late to class. This is also true with “check-ins”. If you plan to give space each class period to discuss the crisis, be sure to inform students ahead of time and give them the option to arrive 10 minutes late to class.  
Resources for leading a class discussion  

https://www.niu.edu/citl/_pdf/leadingclassdiscussions.pdf 
After A Campus Incident: General Talking Points and Conversation Guide 

Taking Care of Yourself  
Faculty and staff from other universities who experienced a campus crisis have said the most important factor in university recovery was ensuring their own rest and wellness. Examples: 

Talk about it with people in your family and work network 
Strive for balance in perspective 
Turn off media and take a break, even briefly 
Honor your feelings 
Help others or do something productive 
Take care of your physical health  

  Resources for caring for yourself after a tragedy 

How to Respond in the Classroom: Moving Forward after Tragedy and Trauma 
https://www.counseling.org/knowledge-center/coping-in-the-aftermath-of-a-shooting 
Tips for College and University Students: Managing Your Distress in the Aftermath 

These resources have been shared by a wide range of MSU faculty and staff, as well as colleagues from other institutions. 

Authors: Jeremy Van Hof, Stephen Thomas, Becky Matz, Kate Sonka, Sarah Wellman, Daniel Trego, Casey Henley, Jessica Knott, David Howe With our guiding principles for remote teaching as flexibility, generosity, and transparency, we know that there is no one solution for assessment that will meet all faculty and student needs.  From this perspective, the primary concern should be assessing how well students have achieved the key learning objectives and determining what objectives are still unmet. It may be necessary to modify the nature of the exam to allow for the differences of the online environment. This document, written for any instructor who typically administers an end-of-semester high-stakes final exam, addresses how best to make those modifications.  In thinking about online exams we recommend the following approaches (in priority order) for adjusting exams: multiple lower-stakes assessments, open-note exams, and online proctored exams.  When changes to the learning environment occur, creating an inclusive and accessible learning experience for students with disabilities should remain a top priority. This includes providing accessible content and implementing student disability accommodations, as well as considering the ways assessment methods might be affected.  
 
Faculty and students should be prepared to discuss accommodation needs that may arise. The team at MSU Resource Center for Persons with Disabilities (RCPD) will be available to answer questions about implementing accommodations. Contact information for Team RCPD is found at https://www.rcpd.msu.edu/teamrcpd. Below you will find a description of each of the recommendations, tips for their implementation, the benefits of each, and references to pertinent research on each.
There are three primary options*: 

Multiple lower-stakes assessments (most preferred)  
Open note exams  (preferred)  
Online proctored exams (if absolutely necessary)

*Performance-based assessments such as laboratory, presentation, music, or art experiences that show proficiency will be discussed in another document



Multiple lower-stakes assessments
Description: The unique circumstances of this semester make it necessary to carefully consider your priorities when assessing students. Rather than being cumulative, a multiple assessment approach makes assessment an incremental process. Students demonstrate their understanding frequently, and accrue points over time, rather than all at once on one test. Dividing the assessment into smaller pieces can reduce anxiety and give students more practice in taking their exams online.  For instance, you might have a quiz at the end of each week that students have to complete. Each subsequent quiz can (and should) build on the previous one, allowing students to build toward more complex and rigorous applications of the content. Using this approach minimizes your need to change the types of questions that you have been asking to date, which can affect student performance (e.g. if you normally ask multiple-choice questions, you can continue to do so).   For the remainder of the semester, use the D2L quizzes tool to build multiple smaller assessments. Spread out the totality of your typical final exam over the month of April. This can be as simple as dividing a 100 question final exam into eight 12-question “synthesis activities” that students complete bi-weekly.
Benefits as noted from the literature: 

No significant differences were observed in terms of keystroke information, rapid guessing, or aggregated scores between proctoring conditions;
More effective method for incentivizing participation and reading; 
Encourages knowledge retention as each subsequent assessment builds on the last

Rios, J. A., & Liu, O. L. (2017). Online proctored versus unproctored low-stakes internet test administration: Is there differential test-taking behavior and performance?. American Journal of Distance Education, 31(4), 226-241. https://www.tandfonline.com/doi/abs/10.1080/08923647.2017.1258628  Schrank, Z. (2016). An assessment of student perceptions and responses to frequent low-stakes testing in introductory sociology classes. Teaching Sociology, 44(2), 118-127. https://journals.sagepub.com/doi/abs/10.1177/0092055X15624745  VanPatten, B., Trego, D., & Hopkins, W. P. (2015). In‐Class vs. Online Testing in University‐Level Language Courses: A Research Report. Foreign Language Annals, 48(4), 659-668. https://onlinelibrary.wiley.com/doi/abs/10.1111/flan.12160 
Open note exams 
Description: Open note assessments allow students to refer to the Internet and other materials while completing their assessments. By design, this disincentives academic dishonesty. Often instructors put time parameters around open note exams. These types of exams also lend themselves to collaborative work in which multiple students work together to complete the assessment. With an open note strategy, you can keep your general exam schedule and point structure, but you may need to revise questions so they are less about factual recall and more about the application of concepts.  For instance you might give students a scenario or case study that they have to apply class concepts to as opposed to asking for specific values or definitions. If you plan to make such changes, communicate your intent and rationale to you students prior to the exam.  One effective open note testing technique is to use multiple-true/false questions as a means to measure understanding. These questions (called “multiple selection” questions in D2L) pose a scenario and prompt students to check all the boxes that apply. For example, students may be prompted to read a short case or lab report, then check all statements that are true about that reading. In this way a single question stem can assess multiple levels of complexity and/or comprehension. 
Benefits as noted from the literature: 



Open-book exams and collaborative exams promote development of critical thinking skills. 
Open-book exams are more engaging and require higher-order thinking skills. 
Application of open-book exams simulates the working environment. 
Students prefer open-book exams and report decreased anxiety levels. 
Collaborative exams stimulate brain cell growth and intricate cognitive complexes.  



Johanns, B., Dinkens, A., & Moore, J. (2017). A systematic review comparing open-book and closed-book examinations: Evaluating effects on development of critical thinking skills. Nurse education in practice, 27, 89-94. https://www.sciencedirect.com/science/article/abs/pii/S1471595317305486
 
Couch, B. A., Hubbard, J. K., & Brassil, C. E. (2018). Multiple–true–false questions reveal the limits of the multiple–choice format for detecting students with incomplete understandings. BioScience, 68(6), 455-463. https://doi.org/10.1093/biosci/biy037 
Implementation for multiple lower-stakes and open note assessment strategies: 

Timed vs. untimed: On the whole, performance on timed and untimed assessments yields similar scores. Students express greater anxiety over timed assessments, while they view untimed assessments as more amenable to dishonest behavior. 

NOTE: If you typically have a time limit on your face-to-face assessments, increase it by 20% to allow for the added demands a remote (distinct from online) environment places on students.


If the exam is meant to be taken synchronously, remember to stay within your class period. Adjust the length of the exam accordingly.
Reduced scope: Decreasing content covered in the exam may be necessary to create an exam of appropriate length and complexity, given the unique circumstances this semester. 
Question pools: Create a pool of questions, and let D2L randomly populate each student’s quiz. This helps reduce dishonest behavior 

For example, a 10 question quiz might have 18 total questions in the pool, 10 of which are randomly distributed to each student by D2L. 


Randomize answer order: In questions in which it makes sense, have D2L randomize the order in which the answer options appear. 
Individual question per page: This can reduce instances of students taking the assessment together. It is even more effective when question order is randomized and a question pool is used. <//li>
Honor code attestation: Give students an opportunity to affirm their intent to be honest by making question one of every assessment a 0-point question asking students to agree to an honor code.  You can access the MSU Honor Code: https://www.deanofstudents.msu.edu/academic-integrity 
Live Zoom availability: In D2L Quizzes, set a time window during which the assessment will be available to students. 
Hold a live open office hours session in Zoom at some point during that window, so that students who want to can take the assessment while they have direct access to you - this way they can ask questions if any arise. 

Ultimately, our guiding principles for online teaching are flexibility, generosity, and transparency.  Try to give students as much of an opportunity to demonstrate their knowledge as possible.  

Consider allowing multiple attempts on an assessment. 
When conditions allow, consider allowing multiple means of expression. 
Can students choose to demonstrate their knowledge from a menu of options

M/C test
Written response
Video presentation 
Oral Exam (via Zoom) 


Consider giving students choices. Perhaps they can opt out of answering a question or two. Perhaps they can choose which of a series of prompts to respond to. Perhaps students can waive one test score (to help accomodate for their rapidly changing environments) 

Proctored assessments 
Description: Respondus Lockdown Browser and Respondus Monitor are tools for remote proctoring in D2L. More information is available at https://help.d2l.msu.edu/node/4686. Please consider whether your assessments can be designed without the need for Respondus. While Respondus may be helpful in limited circumstances (e.g., when assessments must be proctored for accreditation purposes), introducing a new technology may cause additional stress for both students and instructors, and academic integrity is still not assured.   High-stakes exams (those that are a large percentage of a student’s grade) that use new technologies and approaches can decrease student performance and may not reflect students’ understanding of the material.  Please do not use an online proctored approach unless your assessment needs require its use.   
Benefits: 
Increases the barrier to academic dishonesty. Allows for use of existing exams (assuming they are translated in D2L’s Quizzes tool). 
Implementation:

Any online proctored exam must be created and administered using D2L’s Quizzes tool. 
Prior to offering a graded proctored exam, we strongly recommend that you administer an ungraded (or very low-stakes) practice test using the proctoring tool. 
Clear communication with students about system and hardware requirements and timing considerations is required. 
MSU has gained temporary no-cost access to a pair of online proctoring tools provided by Respondus: https://help.d2l.msu.edu/node/4686 
Respondus Lockdown Browser requires that students download a web browser.
When they click into your exam, the Lockdown Browser opens, and prevents users from accessing anything else on their computer. 
Respondus Monitor requires use of Respondus Lockdown Browser and a webcam.
Students are monitored via the webcam while they complete the exam in Lockdown Browser. 

Additional Resources: 

Remote Assessment Quick Guide 
Remote Assessment Video Conversation 
D2L Quizzes Tool Guide
Self-training on D2L Quizzes (login to MSU’s D2L is required; self-enroll into the training course) 

 References: Alessio, H.M.; Malay, N.; Mauere, K.; Bailer, A.J.; & Rubin, B.(2017) Examining the effect of proctoring on online test scores, Online Learning 21 (1)  Altınay, Z. (2017) Evaluating peer learning and assessment in online collaborative learning environments, Behaviour & Information Technology, 36:3, 312-320, DOI: 10.1080/0144929X.2016.1232752 
Couch, B. A., Hubbard, J. K., & Brassil, C. E. (2018). Multiple–true–false questions reveal the limits of the multiple–choice format for detecting students with incomplete understandings. BioScience, 68(6), 455-463. https://doi.org/10.1093/biosci/biy037  Cramp, J.; Medlin, J. F.; Lake, P.; & Sharp, C. (2019) Lessons learned from implementing remotely invigilated online exams, Journal of University Teaching & Learning Practice, 16(1).  Guerrero-Roldán, A., & Noguera, I.(2018) A Model for Aligning Assessment with Competences and Learning Activities in Online Courses, The Internet and Higher Education, vol. 38, pp. 36–46., doi:10.1016/j.iheduc.2018.04.005. 
Johanns, B., Dinkens, A., & Moore, J. (2017). A systematic review comparing open-book and closed-book examinations: Evaluating effects on development of critical thinking skills. Nurse education in practice, 27, 89-94. https://www.sciencedirect.com/science/article/abs/pii/S1471595317305486  Joseph A. Rios, J.A. & Lydia Liu, O.L. (2017) Online Proctored Versus Unproctored Low-Stakes Internet Test Administration: Is There Differential Test-Taking Behavior and Performance?, American Journal of Distance Education, 31:4, 226-241, DOI: 10.1080/08923647.2017.1258628 Schrank, Z. (2016). An assessment of student perceptions and responses to frequent low-stakes testing in introductory sociology classes. Teaching Sociology, 44(2), 118-127. https://journals.sagepub.com/doi/abs/10.1177/0092055X15624745  Soffer, Tal, et al. “(2017) Assessment of Online Academic Courses via Students' Activities and Perceptions, Studies in Educational Evaluation, vol. 54, pp. 83–93., doi:10.1016/j.stueduc.2016.10.001. 
Tan, C.(2020) Beyond high-stakes exam: A neo-Confucian educational programme and its contemporary implications, Educational Philosophy and Theory, 52:2, 137-148, DOI: 10.1080/00131857.2019.1605901 
VanPatten, B., Trego, D., & Hopkins, W. P. (2015). In‐Class vs. Online Testing in University‐Level Language Courses: A Research Report. Foreign Language Annals, 48(4), 659-668. https://onlinelibrary.wiley.com/doi/abs/10.1111/flan.12160 

(Photo by Steve Johnson on Unsplash )
You can also access the Generative AI Syllabus Guide Playlist with this content broken down into the following sections. Table of Contents:

MSU Guidance and [Non]Permitted Uses
Developing and Communicating a Course-level Generative AI Use policy
Example Syllabus Statements for the Use of AI Tools in Your Course

Design For Generative AI (restrict, permit, require)
Design Around Generative AI (ban)


Example Statements from Current USA, Higher Education Educators
Developing your Scholarly and Ethical Approaches to Generative AI
Beyond Syllabi Language
Additional considerations to help you develop your generative AI philosophy (Watkins, 2022)
References

The following MSU-specifics should be used to inform your decisions...
Overall guidance: We collectively share the responsibility to uphold intellectual honesty and scholarly integrity. These are core principles that may be compromised by the misuse of GenAI tools, particularly when GenAI-generated content is presented as original, human-created work.  
Permitted uses in Teaching & Learning: Instructors are expected to establish a course-specific guidance that defines the appropriate and inappropriate use of GenAI tools.

Students may only use GenAI tools to support their coursework in ways explicitly permitted by the instructor.  

Non-permissible uses: 

Do not Use GenAI to deliberately fabricate, falsify, impersonate, or mislead, unless explicitly approved for instruction or research in a controlled environment.
Do not Record or process sensitive, confidential, or regulated information withnon-MSU GenAI tools.
Do not Enter FERPA-protected student records, PII, PHI, financial, or HR data into unapproved tools; comply with MSU’s data policy and all regulations.
Do not Use export-controlled data or CUI with GenAI tools unless approved for MSU’s Regulated Research Enclave (RRE).

Developing and Communicating a Course-level Generative AI Use policy 
A well-prepared course should be designed for ("restrict", "permit" or "require") or designed around ("ban") generative AI. Courses designed for AI should detail the ways and degrees to which generative AI use will be incorporated into activities and assessments. Courses designed for AI may incorporate AI for some activities and not others and depending on course AI may be explicitly excluded or included at different stages. Courses designed around AI may discuss impacts of generative AI as a topic but expectations are that students will not use these types of tools, and the course should be intentionally designed such that the use of generative AI would either not be conducive to the completion of assessments and activities, or such that the attempt to do so would prove overly cumbersome. 
Regardless of your approach, communicating your expectations and rationale to learners is imperative.
Set clear expectations. Be clear in your syllabus about your policies for when, where, and how students should be using generative AI tools, and how to appropriately acknowledge (e.g., cite, reference) when they do use generative AI tools. If you are requiring students to use generative AI tools, these expectations should also be communicated in the syllabus and if students are incurring costs, these should be detailed in the course description on the Registrar’s website. 
Regardless of your approach, you might include time for ethics discussions. Add time into your course to discuss the ethical implications of chatGPT and forthcoming AI systems. Talk with students about the ethics of using generative AI tools in your course, at your university, and within your discipline or profession. Don’t be afraid to discuss the gray areas where we do not yet have clear guidance or answers; gray areas are often the places where learning becomes most engaging.
Example Syllabus Statements for the Use of AI Tools in Your Course 
There is no “one size fits all policy” for AI uses in higher education. Much like attendance/participation policies, GenAI course-level rules and statements will be determined by individual instructors, departments, and programs. The following resource is provided to assist you in developing coherent policies on the use of generative AI tools in your course, within MSU's guideline. Please adjust these examples to fit your particular context. Remember communication of your course generative AI policies should not only be listed in your syllabus, but also explicitly included  in assignment descriptions where AI use is allowed or disallowed. 
It is your responsibility as instructor to note and explain your individual course-level rule. A conversation with your department is highly recommended so that generative AI use in the classroom reflects broader use in the unit and  discipline. If you have specific questions about writing your course rules, please reach out to the Center for Teaching and Learning Innovation. 
Design For Generative AI
Restrict [This syllabus statement is useful when you are allowing the use of AI tools for certain purposes, but not for others. Adjust this statement to reflect your particular parameters of acceptable use. The following is an example.] 
Example1:  

The use of generative AI tools (e.g. ChatGPT, Dall-e, etc.) is permitted in this course for the following activities:

[insert permitted your course activities here*]


The use of generative AI tools is not permitted in this course for the following activities:

[insert not permitted your course activities here*]


You are responsible for the information you submit based on an AI query (for instance, that it does not violate intellectual property laws, or contain misinformation or unethical content). Your use of AI tools must be properly documented and cited in order to stay within university policies on academic integrity and the Spartan Code of Honor Academic Pledge.

Example2: Taken, with slight modification, from Temple University’s Center for the Advancement of Teaching  to demonstrate the kinds of permitted/restricted activity an instructor could denote.
The use of generative AI tools (e.g. ChatGPT, Dall-e, etc.) is permitted in this course for the following activities: 



Brainstorming and refining your ideas; 
Fine tuning your research questions; 
Finding information on your topic; 
Drafting an outline to organize your thoughts; and 
Checking grammar and style. 



The use of generative AI tools is not permitted in this course for the following activities: 



Impersonating you in classroom contexts, such as by using the tool to compose discussion board prompts assigned to you or content that you put into a Zoom chat. 
Completing group work that your group has assigned to you, unless it is mutually agreed within your group and in alignment with course policy that you may utilize the tool. 
Writing a draft of a writing assignment. 
Writing entire sentences, paragraphs or papers to complete class assignments. 



You are responsible for the information you submit based on an AI query (for instance, that it does not violate intellectual property laws, or contain misinformation or unethical content). Your use of AI tools must be properly documented and cited in order to stay within university policies on academic integrity and the Spartan Code of Honor Academic Pledge. For example, [Insert citation style for your discipline. See these resources for APA guidance, and for other citation formats.]. Any assignment that is found to have used generative AI tools in unauthorized ways [insert the penalty here*]. When in doubt about permitted usage, please ask for clarification. 
 
Use permitted [This syllabus statement is useful when you are allowing, and perhaps encouraging, broad use of generative AI tools. Adjust this statement to reflect your particular parameters of acceptable use in your course. The following is an example.] 
Example:
You are welcome to use generative AI tools (e.g. ChatGPT, Dall-e, etc.) in this class as doing so aligns with the course learning goal [insert the course learning goal use of AI aligns with here*]. You are responsible for the information you submit based on an AI query (for instance, that it does not violate intellectual property laws, or contain misinformation or unethical content). Your use of AI tools must be properly documented and cited in order to stay within university policies on academic integrity and the Spartan Code of Honor Academic Pledge.
Use required [This syllabus statement is useful when you have certain assignments that will require that students use generative AI tools. Adjust this statement to reflect your particular parameters of acceptable use. The following is an example.] 
Example:
You will be expected to use generative AI tools (e.g. ChatGPT, Dall-e, etc.) in this class as doing so aligns with the course learning goal [insert the course learning goal use of AI aligns with]. Our class will make use of the [insert name of tool(s) here*] tool, and you can gain access to it by [insert instructions for accessing tool(s) here*]. You are responsible for the information you submit based on an AI query (for instance, that it does not violate intellectual property laws, or contain misinformation or unethical content). Your use of AI tools must be properly documented and cited in order to stay within university policies on academic integrity and the Spartan Code of Honor Academic Pledge.
 
Design Around Generative AI
Ban [This syllabus statement is useful when you are forbidding all use of generative AI tools for any purpose in your class. Adjust this statement to reflect your particular parameters of acceptable use. The following is an example.] 
The use of generative AI tools (such as ChatGPT, DALL-E, etc.) is not permitted in this class; therefore, any use of AI tools for work in this class may be considered a violation of Michigan State University’s policy on academic integrity, the Spartan Code of Honor Academic Pledge andStudent Rights and Responsibilities, since the work is not your own. The use of unauthorized AI tools will result in [insert the penalty here*].

CONCERN: The ubiquity of generative AI tools, including their integration into Google search results and MS Office products, means that an outright generative AI ban is implausible for any activity that makes use of the Internet or MS Office Suite.

* It is highly recommended that you have conversations in your department about the appropriate penalties for unauthorized use of an AI. It is important to think about the appropriate level of penalty for first-time offenders and those who repeatedly violate your policies on the use of AI. 



Example Statements from Current USA, Higher Education Educators
This collection of example statements are a compilation from a variety of sources including Faculty Learning Community (FLC) at Cleveland State University, Ohio University’s AI, ChatGPT and Teaching and Learning, and some of Michigan State University’s own educators! (If you have an example generative AI policy from your course that you’d be willing to share, please add it to the comments below or e-mail it to MSU Center for Teaching and Learning Innovation at teaching@msu.edu) NOTE: making your own course-level determination of "ban", "restrict", "permit", or "require" and using the sample language is the best, first place to start!
 “AI (artificial intelligence) resources such as ChatGPT can be useful in a number of ways. Because it can also be abused, however, you are required to acknowledge use of AI in any work you submit for class. Text directly copied from AI sites must be treated as any other direct quote and properly cited. Other uses of AI must be clearly described at the end of your assignment.” -Claire Hughes-Lynch
 “While AI tools can be useful for completing assignments and detecting plagiarism, it is important to use them responsibly and ethically. Practice based on these guidelines as a future or current K-12 teacher. The following are some guidelines for what not to do when using AI in your assignments and for plagiarism detection:

Do not rely solely on AI tools to complete assignments. It is important to understand the material and complete assignments on your own, using AI tools as a supplement rather than a replacement for your own work.
Do not use AI tools to plagiarize*. Using AI to generate or modify content to evade plagiarism detection is unethical and violates academic integrity.
Do not assume that AI responses are always correct. It has been noted that AI can generate fake results.* Please see the plagiarism/academic integrity policy in the course syllabus.” -Selma Koc

“Intellectual honesty is vital to an academic community and for my fair evaluation of your work. All work submitted in this course must be your own, completed in accordance with the University’s academic regulations. Use of AI tools, including ChatGPT, is permitted in this course. Nevertheless, you are only encouraged to use AI tools to help brainstorm assignments or projects or to revise existing work you have written. It is solely your responsibility to make all submitted work your own, maintain academic integrity, and avoid any type of plagiarism. Be aware that the accuracy or quality of AI generated content may not meet the standards of this course, even if you only incorporate such content partially and after substantial paraphrasing, modification and/or editing. Also keep in mind that AI generated content may not provide appropriate or clear attribution to the author(s) of the original sources, while most written assignments in this course require you to find and incorporate highly relevant peer-reviewed scholarly publications following guidelines in the latest publication manual of the APA. Lastly, as your instructor, I reserve the right to use various plagiarism checking tools in evaluating your work, including those screening for AI-generated content, and impose consequences accordingly.” -Xiongyi Liu
“If you are ever unsure about whether collaboration with others, including using artificial intelligence, is allowed or not, please ask me right away. For the labs, although you may discuss them in groups (and try using AI), you must all create your own code, output and answers. Quizzes will be done in class and must be solely your own work. You alone are always responsible for the correctness of the final answers and assignments you submit.” - Emily Rauschert on AI as collaboration partner
“Chat GPT: The use of Chat GTP is neither encouraged nor prohibited from use on assignments for GAD 250. Chat GPT is quickly becoming a communication tool in most business settings. Therefore, if you choose to use Chat GPT for assignments, please be sure to revise the content for clarity, conciseness, and audience awareness. Chat GPT is simply a tool and should not be used as a way to produce first and only drafts. Every assignment submission will be graded using the rubric provided in the syllabus. Be aware that Chat GPT may not develop high-quality work that earns a passing grade. It is your responsibility to review and revise all work before submitting to the instructor.” -Leah Schell-Barber for a Business Communications Course
“Use of Generative AI, such as ChatGPT and Microsoft Bing-Chat, must maintain the highest standards of academic integrity and adhere to the OU Code of Student Conduct.  The use of Generative AI should be seen as a tool to enhance academic research, not as a replacement for critical thinking and originality in assignments. Students are not permitted to submit assignments that have been fully or partially generated by AI unless explicitly stated in the assignment instructions. All work submitted must be the original work of the student. Any ideas garnered from Generative AI research must be acknowledged with proper in-text citation and reference. Students may be asked to save the AI chat as a PDF file for verification.” -Ohio University College of Business Generative AI Use for Academic Work Policy
“‘The policy of this class is that you must be the creator of all work you submit for a grade. The use of others’ work, or the use of intelligent agents, chat bots, or a.i. engines to create your work is a violation of this policy and will be addressed as per MSU and Broad College codes of conduct.’ - Jeremy Van Hof… Or, you might consider this, which I asked ChatGPT to write for me: ‘Sample Policy Language: Students should not use ChatGPT to complete course assignments or for any other academic activities. ChatGPT should be used as a supplemental resource and should not replace traditional academic activities.’ (ChatGPT per Jeremy Van Hof’s prompting) 
Or this much longer version, also written by ChatGPT: ‘The following course policy statement prohibits the use of Artificial Intelligence (AI) for the’ completion of assignments and activities during the duration of the course. At the Broad College, we strive to create an academic environment where learning is the foremost priority. We strongly believe that learning is best achieved through the hard work and dedication of our students. As such, we prohibit the use of Artificial Intelligence (AI) for the completion of assignments and activities during the course.  Our policy is in line with our commitment to providing a fair and equitable learning environment for all students. We believe that AI should not be used to substitute human effort, as it defeats the purpose of our educational goals, which are to encourage critical thinking and problem-solving.  We understand that AI can be a useful tool in many contexts, and we do not discourage its use in other courses. However, in this course, we will not accept assignments or activities that have been completed through the use of AI. We expect our students to be honest and to complete their work independently.  We will be monitoring student work closely to ensure compliance with this policy. Violations of this policy will be met with disciplinary sanctions. All students are expected to adhere to this policy and to abide by the standards of the University.’ (ChatGPT per Jeremy Van Hof’s prompting)” -Jeremy Van Hof, Broad College of Business
“I study AI. I research it in my role as faculty in the Experience Architecture and Professional & Public Writing majors. And I don’t think it’s inherently bad or scary, in the same way that a calculator isn’t bad/scary for math. Artificial intelligence technologies such as ChatGPT can be an excellent starting point and a place to begin inquiry. But they are not a replacement for human thinking and learning. Robots lack empathy and nuance. As such, here is my policy:
You may use AI as a tool, but you may not use AI to replace your own beautiful brain. That means that you may ask ChatGPT, for example, to give you a list of bands similar to one that you hear and appreciate in this course. You may ask ChatGPT to give you an overview of a punk scene in a geographic location at a particular time. You may ask it for the history of punk rock and punk cultures. You may ask it what happened to Sid Vicious. 
But you may not ask it to write on your behalf, and you must not turn in anything that has been written by ChatGPT and pass it off as your own for any assignment in this class, including discussion responses, papers, and exams. If you do so, I will know, and that will lead to an uncomfortable moment–and to you failing the assignment.
This is not meant to be punitive. It’s meant to reinforce how much I value you and your ideas and your intellect. In a face-to-face environment, we would have a lengthy conversation about AI, ethics, and human learning. If you want to have that conversation, I’m happy to do so via Zoom–email me!” -Kate Birdsall, asynchronous US23 course on punk-rock politics
Developing your Scholarly and Ethical Approaches to Generative AI 
Taken, with slight modification, from “Update Your Course Syllabus for chatGPT” by Ryan Watkins, Professor of Educational Technology Leadership, and Human-Technology Collaboration at George Washington University in Washington DC (2022), via Medium. 
Beyond Syllabi Language 
Communicate your perspective about AI use. In addition to syllabus statements, consider talking with your students about AI tools like ChatGPT. Regardless of your orientation to generative AI use, it is important that you clearly communicate your expectations with the introduction of each assignment/assessment.   
Different levels of familiarity: As an emerging technology, students will have differing levels of familiarity with these tools. For instance, while ChatGPT can write a grammatically correct paper or appear to solve a math problem, it may be unreliable and limited in scope. Discuss with students the uses and limitations of AI tools more broadly in addition to your perspective on their use in your class. 
Connect to critical thinking skills: AI tools have many implications beyond the classroom. Consider talking with students about how to be engaged-consumers of AI content (e.g., how to identify trusted sources, reading critically, privacy concerns). Discuss how you and colleagues use AI in your own work.
Adapt assessments. AI tools are emerging and it can be incredibly difficult to make any assessment completely free from AI interference. Beyond a syllabus statement, you may also consider adapting your assessments to help reduce the usefulness of AI products. However before revising any assignment, it’s helpful to reflect on what exactly you want students to get out of the experience and share your expectations with your students. Is it just the end product, or does the process of creating the product play a significant role? 

Create assessments that allow students to develop ideas over time. Depending on your class size, consider scaffolding assessments to be completed in small components (e.g., proposal, annotated bibliography, outline, first draft, revised drafts). 
Ask students to connect their writing to specific course materials or current events. Students can draw from the course textbook, additional readings on Moodle or Blackboard, and even class discussion boards or in-class discussions.  
Incorporate personal experiences and reflections. Provide students with opportunities to connect what they are learning to their own lives and experiences—stories unique to each individual. 
Incorporate Multimedia Assessments. Consider developing or adapting assessments to include multimedia submissions (e.g., audio or video components). Also, consider peer-review and social annotation tools like Eli Review or Google Docs for students to use when responding to assigned readings or other materials.  
Use class time. Ask students to complete writing assignments during class time (e.g. complete reading reflections at the beginning of class, or use exit tickets). Asking students to organize their ideas by writing during class may also support student engagement in other class activities such as discussions and group work.  

Get Creative With Your Assignments: Visit “Update Your Course Syllabus for chatGPT” by Ryan Watkins (Medium article) for 10 ideas for creative assignments adapted for a classroom with chatGPT. You can mitigate the risk of students using chatGPT to cheat, and at the same time improve their knowledge and skills for appropriately using new AI technologies inside and outside the classroom.
Additional considerations to help you develop your generative AI philosophy (Watkins, 2022)
Expand your options. Consider your repertoire of instructional strategies. Atsusi Hirumi offers a guide to research-grounded strategies for any classroom. These are not, however, “a la carte” menus; you must use all of the steps of any strategy to gain the evidence-based benefits.
Reflect on your values. As Tyler Cowen pointed out, there will be those who gain and those that lose with the emergence of chatGPT and other generative AI tools. This is as true for students as it is for faculty and instructors. Be ready to openly discuss the ethical implications of generative AI tools with your students, along with the value of what you are teaching and why learning these are important to their futures.
Consider time. As discussed during Bryan Alexander’s webinar, chatGPT and other generative AI tools offer a short-cut to individuals who are short on time. Examine your course schedule to determine if you are unknowingly pushing students to take short-cuts. Some instructors try to cover too much content in their courses already.
Remember, AI is not human. Be careful not to anthropomorphize chatGPT and other generative AI tools. ChatGPT is a language model, and if we anthropomorphize these technologies, then it will be much harder to understand their promise and perils. Murray Shanahan suggests that we avoid statements such as, “chatGPT knows…”, or “ChatGPT thinks…”; instead, use “According to chatGPT…” or “ChatGPT’s output…”.
Again, AI is likely to be a part of your students’ life to some extent this semester, so plan accordingly. Critically considering your course design in the context of generative AI is an important educator practice. Following the Provost’s call, MSU instructors are encouraged to 1) develop a course-level generative AI use policy and actively discuss with students about expectations for generative AI use in the work for your class, 2) promote equitable and inclusive use of the technology, and 3) work with colleagues across campus to determine ethical and scholarly applications of generative AI for preparing students to succeed in an evolving digital landscape. MSU does not currently have a university-wide policy on AI in the classroom, so  it is your responsibility as instructor to note and explain your individual course policy. A conversation with your department is highly recommended so that generative AI use in the classroom reflects that in the discipline. 
References
This resource is collated from multiple sites, publications, and authors with some modification for MSU context and links to MSU specific resources. Educators should always defer to University policy and guidelines. 

MSU Office of Student Support & Accountability Faculty Resources, including Academic Dishonesty Report form. 
Watkins, R. (2022) Update Your Course Syllabus for chatGPT. Educational Technology Leadership, The George Washington University via Medium: https://medium.com/@rwatkins_7167/updating-your-course-syllabus-for-chatgpt-965f4b57b003 
Center for the Advancement of Teaching (2023). Sample Syllabus Statements for the Use of AI Tools in Your Course. Temple University 
Center for Teaching & Learning (2023) How Do I Consider the Impact of AI Tools like ChatGPT in My Courses?. University of Massachusetts Amherst. https://www.umass.edu/ctl/how-do-i-consider-impact-ai-tools-chatgpt-my-courses 
Center for Teaching, Learning and Assessment (2023). AI, ChatGPT and Teaching and Learning. Ohio University. https://www.ohio.edu/center-teaching-learning/instructor-resources/chat-gpt
Office of Teaching, Learning, and Technology. (2023). Artificial Intelligence Tools and Teaching. Iowa University. https://teach.its.uiowa.edu/artificial-intelligence-tools-and-teaching 
Center for New Designs in Learning and Scholarship (2023). Chat GPT and Artificial Intelligence Tools. Georgetown University. https://cndls.georgetown.edu/ai-composition-tools/#privacy-and-data-collection 
Office for Faculty Excellence (2023). Practical Responses to ChatGPT. Montclair State University. https://www.montclair.edu/faculty-excellence/practical-responses-to-chat-gpt/ 
Teaching and Learning at Cleveland State University by Center for Faculty Excellence is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License