We found 296 results that contain "#ai-iah"
Posted on: GenAI & Education
AI for MSU Educators
This playlist, developed by the Instructional Technology and Development Team at IT, includes some general and MSU-specific resources about using ChatGPT and similar AI tools in teaching and learning. Currently, it consists of a list of FAQs about ChatGPT and an interactive Padlet site for you to share your experiences with AI and get connected with other MSU educators.
Posted on: CISAH
IAH Kickoff 2022 (08/26/22)
Recording and resources from the IAH Kickoff meeting on August 26, 2022
NAVIGATING CONTEXT
Posted on: GenAI & Education
Using AI in Teaching & Learning
Resources for exploring the use of AI, and specifically large language models similar to ChatGPT, in teaching and learning. This is inclusive of its uses for instructors (e.g., lesson planning, rubric generation, etc.) and for students (e.g., writing assignments, comparison exercises, etc.)
Posted on: GenAI & Education
Generative Artificial Intelligence (AI) Guidance from MSU
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.
You can learn more about generative AI and participate in discussions here on iteach.msu.edu.
Click section 3 of this playlist for additional resources on generative AI on #iteachmsu!
You can learn more about generative AI and participate in discussions here on iteach.msu.edu.
Click section 3 of this playlist for additional resources on generative AI on #iteachmsu!
Posted on: GenAI & Education
Generative AI Syllabus Guide
A good portion of your students will likely use AI to some extent this semester, so plan accordingly. Many students are aware of generative AI, and at least some of them will use these tools for their course work. Critically considering your course design in the context of generative AI is an important educator practice.
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 with
non-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).
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 with
non-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).
PEDAGOGICAL DESIGN
Posted on: #iteachmsu
Fairytale Oral Exam IAH 207
Oral exams (or viva voce) are practiced in many disciplines. In the course of an oral exam, an examiner poses questions to a student; the student then has to answer the questions orally, thereby demonstrating their knowledge of the subject matter. In this class, you will participate in a “collaborative” oral exam, where you will prepare as a team to answer a series of open-ended questions (see below) in a discussion format. The purpose of this assignment is to assess your ability to:
Apply close-reading skills developed in class;
Synthesize ideas encountered in course materials and class discussion to create original arguments andinterpretations; and
Practice conversation, collaboration, and consensus
Apply close-reading skills developed in class;
Synthesize ideas encountered in course materials and class discussion to create original arguments andinterpretations; and
Practice conversation, collaboration, and consensus
Authored by: Nicola Imbracsio
Assessing Learning
Posted on: #iteachmsu
Fairytale Exam Sheet IAH 207
This exam sheet corresponds to the Fairtale Oral Exam.
Authored by: Nicola Imbracsio
Assessing Learning
Posted on: GenAI & Education
Promote Equitable and Inclusive Use in Generative AI
Consider equity and inclusion when making decisions about AI use in your course.
How does the development and use of generative AI affect identity groups differentially? What biases exist within the development and use of generative AI? What are the potential challenges regarding AI from an equity-lens (e.g., historic issues with facial recognition and BIPOC populations)?
What data sources does generative AI use to generate a response, and how representative is this data source?
Consider how AI content and perspectives can enhance dialogue and collaboration between diverse disciplines, departments, and individuals.
Consider how integration of generative AI technologies into the classroom help or hinder students’ success.
Consider situations in which some students may have access to more advanced technology than others based on cost or other factors.
Consider if generative AI technology provides accommodation for certain populations and how its use may help achieve equity for persons with disabilities.
Photo by Pietro Jeng on Unsplash
How does the development and use of generative AI affect identity groups differentially? What biases exist within the development and use of generative AI? What are the potential challenges regarding AI from an equity-lens (e.g., historic issues with facial recognition and BIPOC populations)?
What data sources does generative AI use to generate a response, and how representative is this data source?
Consider how AI content and perspectives can enhance dialogue and collaboration between diverse disciplines, departments, and individuals.
Consider how integration of generative AI technologies into the classroom help or hinder students’ success.
Consider situations in which some students may have access to more advanced technology than others based on cost or other factors.
Consider if generative AI technology provides accommodation for certain populations and how its use may help achieve equity for persons with disabilities.
Photo by Pietro Jeng on Unsplash
Posted by: Makena Neal
Posted on: #iteachmsu
Fairytale Oral Exam Rubric IAH 207
This is a rubric that corresponds to the Fairytale Oral Exam.
Authored by: Nicola Imbracsio
Assessing Learning
Posted on: CISAH
UDL in IAH Workshop (10-14-22)
UDL in IAH: Universal Design for Learning and General Education
Slides and Transcript
Google Slides
Zoom Chat Transcript
Zoom Recording
Workshop Links
Please use the links below to complete activities during the workshop. The link needed for each activity will be indicated in the bottom right corner of the related slides.
Universal Design for Learning (UDL) Guidelines
UDL Implementation Rubric
UDL Syllabus Evaluation Rubric
Learning Goal Brainstorm
Workshop Feedback
Additional Resources
Accessibility Checklist (MSU Web Accessibility)
Assistive Technology (MSU Resource Center for Persons with Disabilities)
Establishing Learning Goals (Harriet W. Sheridan Center for Teaching and Learning at Brown)
On Learning Goals and Learning Objectives (Derek Bok Center for Teaching and Learning at Harvard)
How to Write Learning Goals (Stanford Evaluation and Research)
Slides and Transcript
Google Slides
Zoom Chat Transcript
Zoom Recording
Workshop Links
Please use the links below to complete activities during the workshop. The link needed for each activity will be indicated in the bottom right corner of the related slides.
Universal Design for Learning (UDL) Guidelines
UDL Implementation Rubric
UDL Syllabus Evaluation Rubric
Learning Goal Brainstorm
Workshop Feedback
Additional Resources
Accessibility Checklist (MSU Web Accessibility)
Assistive Technology (MSU Resource Center for Persons with Disabilities)
Establishing Learning Goals (Harriet W. Sheridan Center for Teaching and Learning at Brown)
On Learning Goals and Learning Objectives (Derek Bok Center for Teaching and Learning at Harvard)
How to Write Learning Goals (Stanford Evaluation and Research)
Posted by: Garth J Sabo
Pedagogical Design
Posted on: IT - Educational Te...
AI Commons
The AI Commons is a collaborative hub for the MSU community to contribute, discuss, and explore the evolving role of generative AI in higher education. Learn more and share your experiences at https://aicommons.commons.msu.edu/.
Posted by: Lindsay Tigue
Posted on: GenAI & Education
Generative AI Use Codes
The following is a proposed system of “Generative AI Use Codes” (GAUC) for academic assignments to provide clearer communication between instructors and students. These can be used to communicate the allowed level of generative AI assistance and desired degree of citation in academic tasks. The codes are meant to be simple and easy to use, reminiscent of the approach of Creative Commons licenses. There are two parts to the code: Part 1 communicates the role of AI in the task, and Part 2 communicates the desired attribution of the work requested.
Part 1: Generative AI Use Codes (GAUC)
GAUC-0: No Generative AI Allowed
Symbol: AI 🚫
Description: Students are not permitted to use generative AI in any capacity for the assignment.
GAUC-1: Generative AI for Brainstorming Only
Symbol: AI ⛈️
Description: Students can use generative AI for brainstorming ideas, but the final content must be entirely their own.
GAUC-2: Generative AI as a Reference
Symbol: AI 📚
Description: Students can use generative AI as a reference, similar to how one might use a textbook. However, direct output from the AI should not be included verbatim in the final assignment.
GAUC-3: Generative AI for Editing and Refinement
Symbol: AI ✍️
Description: Students can draft their own work and use generative AI tools to edit, refine, and polish their content. The initial ideas and content must originate from the student.
GAUC-4: Collaborative Creation with Generative AI
Symbol: AI 🤝
Description: Students can collaborate with generative AI to create content. While students should be actively involved in the creation process, they can interweave their own content with content generated by the AI.
GAUC-5: Unrestricted Generative AI Use
Symbol: AI 🌍
Description: Students can use generative AI in any capacity, including generating the entirety of the assignment with the AI. They’re encouraged to experiment and innovate using the technology.
Part 2: Generative AI Attribution Codes (GAAC)
N: No Attribution Required
Symbol: 🆓
Description: Students are not required to provide any citation or acknowledgment for using generative AI, irrespective of the extent of AI’s contribution.
S: Source Attribution Required
Symbol: 🔗
Description: Students are required to mention the AI tool or platform they used (e.g., OpenAI’s GPT-4), but no specific citation format is mandated.
C: Comprehensive Attribution Required
Symbol: 📝
Description: Students should provide a comprehensive citation, detailing not just the AI platform/tool, but also specifying parameters, prompts, or any other specifics of how the AI was utilized.
R: Reflection on AI Use
Symbol: 💭
Description: Beyond merely citing the tool, students need to include a short reflection or description of how the AI was used, its influence on the outcome, and any human-AI collaborative dynamics involved.
Implementation:
Example: On assignment sheets or syllabi, faculty can employ both the GAUC and GAAC codes side by side, for instance, “GAUC-3-C” or “AI✍️📝”. This would indicate that students can use generative AI for editing and refinement, and they need to provide comprehensive attribution for the AI used.
Educational Materials: In addition to the code, it would be beneficial to provide students with a brief guide or overview of the GAUC system, explaining each code and its implications. This could include examples of how to cite or reflect on AI use appropriately.
Honor Code Integration: The concept of proper attribution, even to AI tools, should be ingrained in academic integrity guidelines. Stressing the importance of honest and transparent communication regarding AI assistance aligns with principles of academic honesty.
Faculty Discretion: While these codes provide a structured approach, faculty should retain the discretion to make specific clarifications or exceptions based on the nature of the assignment or the objectives of the exercise.
GAUC – 4S – OpenAI. (2023). ChatGPT (Aug 3rd version) [Large language model]. https://chat.openai.com/chat
Part 1: Generative AI Use Codes (GAUC)
GAUC-0: No Generative AI Allowed
Symbol: AI 🚫
Description: Students are not permitted to use generative AI in any capacity for the assignment.
GAUC-1: Generative AI for Brainstorming Only
Symbol: AI ⛈️
Description: Students can use generative AI for brainstorming ideas, but the final content must be entirely their own.
GAUC-2: Generative AI as a Reference
Symbol: AI 📚
Description: Students can use generative AI as a reference, similar to how one might use a textbook. However, direct output from the AI should not be included verbatim in the final assignment.
GAUC-3: Generative AI for Editing and Refinement
Symbol: AI ✍️
Description: Students can draft their own work and use generative AI tools to edit, refine, and polish their content. The initial ideas and content must originate from the student.
GAUC-4: Collaborative Creation with Generative AI
Symbol: AI 🤝
Description: Students can collaborate with generative AI to create content. While students should be actively involved in the creation process, they can interweave their own content with content generated by the AI.
GAUC-5: Unrestricted Generative AI Use
Symbol: AI 🌍
Description: Students can use generative AI in any capacity, including generating the entirety of the assignment with the AI. They’re encouraged to experiment and innovate using the technology.
Part 2: Generative AI Attribution Codes (GAAC)
N: No Attribution Required
Symbol: 🆓
Description: Students are not required to provide any citation or acknowledgment for using generative AI, irrespective of the extent of AI’s contribution.
S: Source Attribution Required
Symbol: 🔗
Description: Students are required to mention the AI tool or platform they used (e.g., OpenAI’s GPT-4), but no specific citation format is mandated.
C: Comprehensive Attribution Required
Symbol: 📝
Description: Students should provide a comprehensive citation, detailing not just the AI platform/tool, but also specifying parameters, prompts, or any other specifics of how the AI was utilized.
R: Reflection on AI Use
Symbol: 💭
Description: Beyond merely citing the tool, students need to include a short reflection or description of how the AI was used, its influence on the outcome, and any human-AI collaborative dynamics involved.
Implementation:
Example: On assignment sheets or syllabi, faculty can employ both the GAUC and GAAC codes side by side, for instance, “GAUC-3-C” or “AI✍️📝”. This would indicate that students can use generative AI for editing and refinement, and they need to provide comprehensive attribution for the AI used.
Educational Materials: In addition to the code, it would be beneficial to provide students with a brief guide or overview of the GAUC system, explaining each code and its implications. This could include examples of how to cite or reflect on AI use appropriately.
Honor Code Integration: The concept of proper attribution, even to AI tools, should be ingrained in academic integrity guidelines. Stressing the importance of honest and transparent communication regarding AI assistance aligns with principles of academic honesty.
Faculty Discretion: While these codes provide a structured approach, faculty should retain the discretion to make specific clarifications or exceptions based on the nature of the assignment or the objectives of the exercise.
GAUC – 4S – OpenAI. (2023). ChatGPT (Aug 3rd version) [Large language model]. https://chat.openai.com/chat
Authored by: Stephen Thomas
Posted on: Center for Teaching...
Citing Generative AI (e.g., ChatGPT) in Higher Education Scholarship, Teaching, and Professional Writing
As generative AI tools like ChatGPT are increasingly used in academic settings—for teaching support, scholarly writing, and even faculty development—it's important to adopt citation practices that are centerend on ethics and that ensure clarity, transparency, and academic integrity. Below are structured guidelines across major citation styles (APA, MLA, Chicago), tailored to the needs of university instructors, researchers, and students. A final section also offers examples of less formal disclosures appropriate for drafts, instructional materials, and academic development work.
Note that as large language models continue to develop, it will become increasingly important to cite the specific model or agent that was used to generate or modify content. It will also be important to regularly revisit citation guidelines, as these, too, are rapidly evolving to meet the demands of the ever-changing AI landscape.
APA (7th ed.) Style
Official Guidance:APA Style Blog: How to Cite ChatGPT
Reference Entry Template:Author. (Year). Title of AI model (Version date) [Description]. Source URL
Example Reference:OpenAI. (2023). ChatGPT (May 24 version) [Large language model]. https://chat.openai.com/
In-text citation:(OpenAI, 2023)
Higher Education Example:When asked to summarize Bandura’s concept of self-efficacy for use in an introductory education course, ChatGPT stated that “self-efficacy refers to an individual’s belief in their ability to execute behaviors necessary to produce specific performance attainments” (OpenAI, 2023).
MLA (9th ed.) Style
Official Guidance:MLA Style Center: Citing Generative AI
Works Cited Template:“[Prompt text]” prompt. ChatGPT, Version Date, OpenAI, Access Date, chat.openai.com.
Example Entry:“Summarize Bandura’s concept of self-efficacy” prompt. ChatGPT, 24 May version, OpenAI, 26 May 2023, chat.openai.com.
In-text citation:("Summarize Bandura’s concept")
Chicago Manual of Style (17th ed.)
Official Guidance:Chicago recommends citing AI-generated text via footnote only, not in the bibliography.
Footnote Example:
Text generated by ChatGPT, May 24, 2023, OpenAI, https://chat.openai.com.
Higher Education Example:
Used in a teaching statement to describe inclusive pedagogy practices. ChatGPT, response to “Give an example of inclusive teaching in STEM,” May 24, 2023, https://chat.openai.com.
Less Formal Disclosures for Transparency
In many instructional or professional academic contexts—such as teaching statements, reflective memos, informal reports, or early-stage drafts—it may be more appropriate to disclose use of generative AI tools in a narrative or parenthetical style rather than a formal citation format. Below are examples of how this can be done responsibly and transparently:
Examples of Less Formal Attribution:
“This draft was developed with the assistance of ChatGPT, which helped generate an outline based on course goals I provided. All final content was authored and reviewed by me.”
“In preparing this teaching philosophy, I used ChatGPT to help articulate distinctions between formative and summative assessment. The generated content was edited and integrated with my personal teaching experiences.”
“Some of the examples included in this workshop description were drafted with the help of ChatGPT (May 2023 version). I adapted the AI-generated responses to better align with our institutional context.”
“This syllabus language on academic integrity was initially drafted using a prompt in ChatGPT. The AI output was revised significantly to reflect course-specific values and policies.”
(Used in slide footnotes or speaking notes): “Initial ideas for this section were generated using ChatGPT and reviewed for accuracy and alignment with our campus policy.”
When to Use Informal Attribution:
Internal memos or reports
Course or assignment drafts
Teaching statements or portfolios
Slide decks or workshop materials
Informal educational publications (e.g., blog posts, teaching commons)
Best Practices for Academic Use in Higher Education
Transparency is key. Whether using a formal citation style or a narrative disclosure, always clearly communicate how AI tools were used.
Human review is essential. AI-generated content should always be edited for accuracy, nuance, inclusivity, and disciplinary alignment.
Tailor to context. Use formal citation when required (e.g., published research); use informal attribution for pedagogical artifacts or collaborative drafts.
Citing Generative AI Content
Citing Generative AI (e.g., ChatGPT) in Higher Education Scholarship, Teaching, and Professional Writing
As generative AI tools like ChatGPT are increasingly used in academic settings—for teaching support, scholarly writing, and even faculty development—it's important to adopt citation practices that are centerend on ethics and that ensure clarity, transparency, and academic integrity. Below are structured guidelines across major citation styles (APA, MLA, Chicago), tailored to the needs of university instructors, researchers, and students. A final section also offers examples of less formal disclosures appropriate for drafts, instructional materials, and academic development work.
Note that as large language models continue to develop, it will become increasingly important to cite the specific model or agent that was used to generate or modify content. It will also be important to regularly revisit citation guidelines, as these, too, are rapidly evolving to meet the demands of the ever-changing AI landscape.
APA (7th ed.) Style
Official Guidance:APA Style Blog: How to Cite ChatGPT
Reference Entry Template:Author. (Year). Title of AI model (Version date) [Description]. Source URL
Example Reference:OpenAI. (2023). ChatGPT (May 24 version) [Large language model]. https://chat.openai.com/
In-text citation:(OpenAI, 2023)
Higher Education Example:When asked to summarize Bandura’s concept of self-efficacy for use in an introductory education course, ChatGPT stated that “self-efficacy refers to an individual’s belief in their ability to execute behaviors necessary to produce specific performance attainments” (OpenAI, 2023).
MLA (9th ed.) Style
Official Guidance:MLA Style Center: Citing Generative AI
Works Cited Template:“[Prompt text]” prompt. ChatGPT, Version Date, OpenAI, Access Date, chat.openai.com.
Example Entry:“Summarize Bandura’s concept of self-efficacy” prompt. ChatGPT, 24 May version, OpenAI, 26 May 2023, chat.openai.com.
In-text citation:("Summarize Bandura’s concept")
Chicago Manual of Style (17th ed.)
Official Guidance:Chicago recommends citing AI-generated text via footnote only, not in the bibliography.
Footnote Example:
Text generated by ChatGPT, May 24, 2023, OpenAI, https://chat.openai.com.
Higher Education Example:
Used in a teaching statement to describe inclusive pedagogy practices. ChatGPT, response to “Give an example of inclusive teaching in STEM,” May 24, 2023, https://chat.openai.com.
Less Formal Disclosures for Transparency
In many instructional or professional academic contexts—such as teaching statements, reflective memos, informal reports, or early-stage drafts—it may be more appropriate to disclose use of generative AI tools in a narrative or parenthetical style rather than a formal citation format. Below are examples of how this can be done responsibly and transparently:
Examples of Less Formal Attribution:
“This draft was developed with the assistance of ChatGPT, which helped generate an outline based on course goals I provided. All final content was authored and reviewed by me.”
“In preparing this teaching philosophy, I used ChatGPT to help articulate distinctions between formative and summative assessment. The generated content was edited and integrated with my personal teaching experiences.”
“Some of the examples included in this workshop description were drafted with the help of ChatGPT (May 2023 version). I adapted the AI-generated responses to better align with our institutional context.”
“This syllabus language on academic integrity was initially drafted using a prompt in ChatGPT. The AI output was revised significantly to reflect course-specific values and policies.”
(Used in slide footnotes or speaking notes): “Initial ideas for this section were generated using ChatGPT and reviewed for accuracy and alignment with our campus policy.”
When to Use Informal Attribution:
Internal memos or reports
Course or assignment drafts
Teaching statements or portfolios
Slide decks or workshop materials
Informal educational publications (e.g., blog posts, teaching commons)
Best Practices for Academic Use in Higher Education
Transparency is key. Whether using a formal citation style or a narrative disclosure, always clearly communicate how AI tools were used.
Human review is essential. AI-generated content should always be edited for accuracy, nuance, inclusivity, and disciplinary alignment.
Tailor to context. Use formal citation when required (e.g., published research); use informal attribution for pedagogical artifacts or collaborative drafts.
Authored by: Jeremy Van Hof
Posted on: CISAH
As part of what will hopefully be an ongoing conversation of the role of AI in teaching the arts and humanities, please consider sharing ways you have integrated (or are interesting in integrating) generative AI like ChatGPT into your IAH teaching.
Posted by: Garth J Sabo
Posted on: CISAH
From 1-2:30 pm on Friday, April 14, CISAH is hosting a workshop titled "AI/IAH: Teaching Arts and Humanities After/With ChatGPT," with options for virtual or in-person (Linton 120) attendance based on your preference. I've attached the workshop flyer for your reference, and there's an RSVP link below for you to let us know if you're planning to come. We hope to see you on the 14th!
RSVP: https://forms.gle/896wfv5GiAwRhxo99
RSVP: https://forms.gle/896wfv5GiAwRhxo99
Posted by: Garth J Sabo
Posted on: CISAH
For any IAH GAs who were unable to attend our orientation meeting today, I've attached a link to our Zoom recording below, along with the slides that we used to guide our discussion. The Zoom Chat transcript is attached as well.
Thanks to everyone who came and participated, especially our GA panelists Nicole Huff and Ames Loji!
Zoom link: https://mediaspace.msu.edu/media/IAH+Fall+2022+GA+Orientation+%28August+25%2C+2022%29/1_3oytg4fe
Slides: https://docs.google.com/presentation/d/108OHv1OcaYJo55aw5usUOwyaAomuL6d2/edit?usp=sharing&ouid=102739642533239513676&rtpof=true&sd=true
Thanks to everyone who came and participated, especially our GA panelists Nicole Huff and Ames Loji!
Zoom link: https://mediaspace.msu.edu/media/IAH+Fall+2022+GA+Orientation+%28August+25%2C+2022%29/1_3oytg4fe
Slides: https://docs.google.com/presentation/d/108OHv1OcaYJo55aw5usUOwyaAomuL6d2/edit?usp=sharing&ouid=102739642533239513676&rtpof=true&sd=true
Posted by: Garth J Sabo
Pedagogical Design
Posted on: #iteachmsu
Use AI to generate rubrics.
To create a rubric for just about anything, I find Chat GPT to be very useful. I use iterations of the following prompts, with specifics for each rubric I need to generate:
"In table form create a rubric with four cut-points ranging from "Not Present" to "Exemplary." There should be XXX number of categories: Category 1, Category 2, Category 3, etc... Leave a column on the left for notes or comments.
Typically, using that prompt as a starting point will lead Chat GPT to creating a workable first draft of a rubric.
ChatGPT provided some other things to consider as you prepare your prompt or modify the results:
"1. Define Clear Objectives: Start by providing the AI with specific objectives or outcomes that the rubric is intended to measure. This could include skills, knowledge, behaviors, or attitudes relevant to the task or subject matter.
2. Input Criteria and Levels of Performance: Give the AI detailed descriptions of the criteria you want to assess, along with different levels of performance (e.g., Excellent, Good, Fair, Poor). Ensure that these descriptions are clear and distinct to guide the AI in creating nuanced and differentiated levels.
3. Incorporate Examples and Standards: To enhance the rubric, include examples of exemplary work or specific standards you expect. This helps the AI to understand the context and quality you're seeking, allowing it to generate more accurate and useful content.
4. Refine and Customize: Once the AI provides a draft, review and refine it to ensure it aligns with your educational goals and standards. Personalize the rubric to the specific needs of your course or assignment, making adjustments based on your expertise and experience."
To create a rubric for just about anything, I find Chat GPT to be very useful. I use iterations of the following prompts, with specifics for each rubric I need to generate:
"In table form create a rubric with four cut-points ranging from "Not Present" to "Exemplary." There should be XXX number of categories: Category 1, Category 2, Category 3, etc... Leave a column on the left for notes or comments.
Typically, using that prompt as a starting point will lead Chat GPT to creating a workable first draft of a rubric.
ChatGPT provided some other things to consider as you prepare your prompt or modify the results:
"1. Define Clear Objectives: Start by providing the AI with specific objectives or outcomes that the rubric is intended to measure. This could include skills, knowledge, behaviors, or attitudes relevant to the task or subject matter.
2. Input Criteria and Levels of Performance: Give the AI detailed descriptions of the criteria you want to assess, along with different levels of performance (e.g., Excellent, Good, Fair, Poor). Ensure that these descriptions are clear and distinct to guide the AI in creating nuanced and differentiated levels.
3. Incorporate Examples and Standards: To enhance the rubric, include examples of exemplary work or specific standards you expect. This helps the AI to understand the context and quality you're seeking, allowing it to generate more accurate and useful content.
4. Refine and Customize: Once the AI provides a draft, review and refine it to ensure it aligns with your educational goals and standards. Personalize the rubric to the specific needs of your course or assignment, making adjustments based on your expertise and experience."
Posted by: Jeremy Van Hof
Pedagogical Design
Posted on: GenAI & Education
AI Commons Bulletin - Human-curated news about generative AI for Teaching and Learning in Higher Education. 12/11/2024
📔 Automatic AI Summaries Now in ProQuest
MSU’s Proquest library database access added an AI “Research Assistant” in an article sidebar. The tool features article summaries, additional sources, important concepts and research topics.
Learn More: Library Learning Space - https://librarylearningspace.com/proquest-launches-ai-powered-research-assistant-to-promote-responsible-ai-use-in-academia/
🔎 Introduction to Prompts
Organizes many practical tips for writing AI prompts into one framework. The article is specific to education and includes links to authoritative resources.
Learn More: Park, J., & Choo, S. (2024). Generative AI Prompt Engineering for Educators: Practical Strategies. Journal of Special Education Technology, 0(0). https://journals.sagepub.com/doi/10.1177/01626434241298954
🧬 Think of AI Uses as Along a Continuum
Monash University describes four examples of AI use in their courses:
1. Explore AI with students to build AI Literacy and discuss academic integrity.
2. Design assessments that focus on process rather than product to build critical thinking.
3. Incorporate new AI-enabled activities, like simulated personas.
4. Use AI for basic assessment, freeing educators to focus on personalized feedback.
Learn More: Hook, J., Junor, A., Sell, C., & Sapsed, C. (2024). Navigating integrity and innovation: Case studies of generative AI integration from an Arts Faculty. ASCILITE Publications, 165–172. https://publications.ascilite.org/index.php/APUB/article/view/1234/1478
Get the AI-Commons Bulletin on our Microsoft Teams channel, at aicommons.commons.msu.edu, or by email (send an email to aicommons@msu.edu with the word “subscribe”).
📔 Automatic AI Summaries Now in ProQuest
MSU’s Proquest library database access added an AI “Research Assistant” in an article sidebar. The tool features article summaries, additional sources, important concepts and research topics.
Learn More: Library Learning Space - https://librarylearningspace.com/proquest-launches-ai-powered-research-assistant-to-promote-responsible-ai-use-in-academia/
🔎 Introduction to Prompts
Organizes many practical tips for writing AI prompts into one framework. The article is specific to education and includes links to authoritative resources.
Learn More: Park, J., & Choo, S. (2024). Generative AI Prompt Engineering for Educators: Practical Strategies. Journal of Special Education Technology, 0(0). https://journals.sagepub.com/doi/10.1177/01626434241298954
🧬 Think of AI Uses as Along a Continuum
Monash University describes four examples of AI use in their courses:
1. Explore AI with students to build AI Literacy and discuss academic integrity.
2. Design assessments that focus on process rather than product to build critical thinking.
3. Incorporate new AI-enabled activities, like simulated personas.
4. Use AI for basic assessment, freeing educators to focus on personalized feedback.
Learn More: Hook, J., Junor, A., Sell, C., & Sapsed, C. (2024). Navigating integrity and innovation: Case studies of generative AI integration from an Arts Faculty. ASCILITE Publications, 165–172. https://publications.ascilite.org/index.php/APUB/article/view/1234/1478
Get the AI-Commons Bulletin on our Microsoft Teams channel, at aicommons.commons.msu.edu, or by email (send an email to aicommons@msu.edu with the word “subscribe”).
Posted by: Michele (MJ) Jackson
Posted on: CISAH
The UDL Guidelines claim learning benefits “for all learners.” How do your accessibility efforts address all students in your IAH class?
Posted by: Garth J Sabo
Pedagogical Design
Posted on: CISAH
The UDL Guidelines aim to reduce barriers and promote challenges. How do you differentiate between barriers and challenges in your IAH class?
Posted by: Garth J Sabo
Pedagogical Design
Posted on: CISAH
Hi folks!
You should see an email going out about this workshop shortly, but I wanted to post some details here as well. On Friday, October 14, from 10-11:30 am, Piril Atabay and I will be facilitating a workshop focusing on the Universal Design for Learning (UDL) framework and the role it plays in IAH general education classes. We'll be running this event with attendance options for in-person (Linton Hall Room 120) and Zoom (details below), and the meeting will be recorded and posted to this iteach page for folks to refer to later.
I've attached the flyer for this event below, as well as an RSVP link and the Zoom credentials for anyone who will be logging in from home. If you have any questions, please feel free to reach out to Piril (atabaypi@msu.edu) or myself (sabogart@msu.edu); otherwise, we hope to see you on the 14th!
- GJS
RSVP: https://forms.gle/jCtGZyQiTtJFCC6p9
Zoom link: https://msu.zoom.us/j/94995585197?pwd=dkFWRWtrYVR4N096QWxFbDJxd1V1Zz09
Meeting ID: 949 9558 5197
Passcode: IAH!
You should see an email going out about this workshop shortly, but I wanted to post some details here as well. On Friday, October 14, from 10-11:30 am, Piril Atabay and I will be facilitating a workshop focusing on the Universal Design for Learning (UDL) framework and the role it plays in IAH general education classes. We'll be running this event with attendance options for in-person (Linton Hall Room 120) and Zoom (details below), and the meeting will be recorded and posted to this iteach page for folks to refer to later.
I've attached the flyer for this event below, as well as an RSVP link and the Zoom credentials for anyone who will be logging in from home. If you have any questions, please feel free to reach out to Piril (atabaypi@msu.edu) or myself (sabogart@msu.edu); otherwise, we hope to see you on the 14th!
- GJS
RSVP: https://forms.gle/jCtGZyQiTtJFCC6p9
Zoom link: https://msu.zoom.us/j/94995585197?pwd=dkFWRWtrYVR4N096QWxFbDJxd1V1Zz09
Meeting ID: 949 9558 5197
Passcode: IAH!
Posted by: Garth J Sabo
Pedagogical Design
Host: CTLI
Introduction to Creating Effective Assessments
This hybrid workshop introduces educators to core strategies for designing effective assessments that support student learning and course goals. Participants will explore various types of assessments, evaluate their alignment with learning objectives, and compare approaches based on course context, including discipline, size, and level. The session will also address the emerging role of generative AI in assessment design, offering insights into both challenges and opportunities in today’s evolving educational landscape.
Upon completion of this learning experience, participants will be able to:
identify various assessments strategies and their types
evaluate whether various assessment types are aligned with a course's objectives
compare different assessment strategies based on course discipline, size, level, and goals
describe the role of generative AI in assessment design.
The in-person location for this session is the Center for Teaching and Learning Innovation. Please join us in the Main Library, Room W207. For directions to W207, please visit the Room Locations page..
Navigating Context
EXPIRED
Host: CTLI
Understanding AI in your pedagogy
This workshop is designed to equip MSU educators with the knowledge and skills necessary to navigate the evolving educational landscape shaped by generative AI. Participants will explore the multifaceted impact of AI on teaching and learning, and develop strategies to integrate AI into their courses effectively while addressing both opportunities and challenges.
Upon completion of this learning experience participants will be able to:
implement AI tools and techniques to enhance teaching practices and improve administrative efficiency in their courses
integrate discussions and content about AI within their discipline to help students understand its relevance and implications in their field of study
develop comprehensive AI policies for their courses, addressing acceptable use, academic integrity, and guidelines for AI-supported assignments and assessments.
Navigating Context
EXPIRED