Artificial Intelligence and Instruction
Implications of recent and rapid advances in the development and availability of generative artificial intelligence (AI) systems such as ChatGPT are resounding across the landscape of teaching and learning. From discussions of academic integrity to efforts in course design, AI is quickly becoming an embedded element of the teaching and learning process that requires the acknowledgement and attention of instructors, instructional designers, and academic leaders.
AI: Opportunities and Challenges for Instruction
The Drake Institute for Teaching and Learning recognizes the emerging opportunities and challenges that many instructors at Ohio State face in understanding, anticipating, and responding to AI in the context of teaching and is committed to supporting the needs of instructors in this space. The Institute also recognizes the highly complex and evolving nature of this issue as one that will ultimately require significant contributions from a large and diverse group of collaborators and stakeholders committed to excellence in teaching and student success in higher education.
Where to Begin?
In response to current concerns and questions of instructors at Ohio State, the Drake Institute, first and foremost, recommends continued reliance on known, evidence-based approaches to instruction in guiding decisions around embracing or otherwise addressing AI in the process of planning, implementing, and evaluating teaching and learning.
To this end, the Institute offers several broad suggestions for instructors and warmly invites questions, conversations, and consultations around these ideas. Use the tabs below to review ideas, recommendations, and examples of strategies and approaches to addressing, incorporating, and establishing expectations around AI in teaching contexts.
To explore even more information on AI as it relates to teaching and learning, please visit the the Ohio State Teaching and Learning Resource Center (TLRC) Teaching Topic page on AI: Considerations for Teaching and Learning. In addition to a summary of teaching-related suggestions, this comprehensive, collaboratively developed resource includes insights and recommendations from a collection of Ohio State units (Drake Institute, OTDI, University Libraries, Center for the Study of Teaching and Writing) around AI platforms, benefits, limitations, and implications for teaching and academic integrity.
Reflecting on course goals and objectives for student learning prior to determining if and how to integrate generative AI applications can help clarify objectives, support alignment with class activities and assessments, and improve student learning and academic performance. Class activities and assignments focused on scaffolding the process of learning, as opposed to those only focused on assessing the product of student learning (e.g., student-developed artifacts such as written assignments, code, or media), may be well-suited to the integration of generative AI applications. The table below offers examples of learning goals, along with examples of how you can potentially use AI applications with students to enhance learning.
Learning Goals | Student Role Example | Instructor Role Example |
Information literacy, collaboration, and value development | Students work in groups to compare AI output vs. human responses; Participate in discussions around style, language, and rhetorical strategies. | Design discussion prompts around the defined learning outcomes in the course; Facilitate discussions. |
Information literacy, collaboration, and evaluation | Students analyze and critique AI-generated responses for missing information, biases, inaccurate content and/or references. | Provide a rubric or work with students to co-develop a rubric for evaluating AI-generated responses. |
Information literacy and critical thinking | Assign students the role of “graders” for AI-generated texts. Students can provide numerical scores based on the course rubric, draft feedback, or write rebuttals. | Provide clear information on task; Guide students toward appropriate approaches (e.g., identify false claims, logical fallacies, fabricated evidence, and unacknowledged biases). |
Knowledge integration, framing questions, and developing a plan for writing | Leverage AI as a tool during early stages of the writing process to brainstorm, brain dump, generate outlines, or develop Initial rough drafts. | Share expectations on use of AI; Explicitly and repeatedly clarify instructions; Consider prompting student reflection on how AI- authored text helped them develop and refine their own compositions. |
Apply course knowledge and skills to edit and revise AI-generated responses | Students practice search strategies and engage in rigorous processes to revise AI- generated works using margin comments and finalizing the draft text into a viable finished product. This exercise reorients the writing process, shifting primary emphasis from composition to revision. | Determine appropriate opportunities to implement. Focus on skill development, as this exercise enables students to practice strategies for generating stronger, more substantive output from AI. This is a skill that might be expected of them as leveraging AI becomes more integrated personal and professional lives. |
As a starting point for drafting course goals and learning objectives, consider building upon existing frameworks (e.g., Fink, 2013; Wiggins & McTighe, 2005), identifying major roadblocks or misconceptions students may encounter during the learning process (Indiana University Bloomington), and involving students in co-designing objectives that address course goals and students’ goals for self-directed learning (Carvalho et al., 2021).
When thinking about supporting learning with potentially transformative technologies such as those involving AI, strive to create learning experiences that enable students to practice in what Bloom (1956) considers, "…the more complex classes of intellectual abilities and skills," (applying, analyzing, evaluating, creating). Often, this is accomplished through careful, evidence-based planning and implementation of active learning strategies, which are discussed in the AI & Active Learning tab.
It is important to acknowledge that thoughtfully considering course goals, learning objectives, and uses of technology to support learning may help enhance or transform your teaching practice (Hilton, 2016). Changes reflective of a fundamental departure from your current practices may result in extensive adjustments to learning activities, assessments, and instructor and student roles. These shifts may necessitate a complete redesign of a course or curriculum. To seek assistance with redesigning courses and assignments, beyond support provided by your department or academic unit, those in the OSU community of instruction should consider registering for a Course Design Institute (CDI) offered by the Drake Institute for Teaching and Learning. You can also contact Teaching and Learning Resource Center partners for further help with addressing your needs.
Active learning has received extensive attention and inquiry across all levels of education. Defined in a variety of ways within the teaching and learning literature, active learning can broadly be described as, "…any instructional method that engages students in the learning process," (Prince, 2004). Recent meta-analyses have helped to establish the value of active learning both in terms of student learning (Freeman et al., 2014) and equity (Theobald et al., 2020).
In considering emerging AI technologies and platforms in the context of active learning, three key suggestions are to:
- Review goals and objectives for student learning, as described in the previous section.
- Plan exercises aimed at helping students develop the knowledge and skills necessary to achieve goals and objectives.
- Carefully implement active learning by using evidence-based approaches that enhance equity and effectiveness, while minimizing student resistance.
To aid the planning and implementation of active learning that embraces or accounts for generative AI applications, consider adopting any combination of the following evidence-based strategies provided in the table below. This table has been constructed based upon insights offered by Nguyen et al. (2021) and Andrews et al. (2022).
Active Learning Strategies | Ideas to Implement While Planning Active Learning with AI | Ideas to Implement Early in the Term | Ideas to Implement During or After Specific AI-Integrated Activities |
Planning Strategies: Develop suitable AI- integrated activities | Return to backward design and align course goals, learning objectives, outcomes, assessments, and activities to inform decisions about if, when, and how to embrace, allow, limit, or exclude AI tools and resources. | Plan to articulate course, activity, or group policies on AI, ideally created in collaboration with students. | Plan for collection of student feedback on their perceptions of value and self-reported use of AI as a learning tool in the course. Use student feedback to iterate over time. |
Explanation Strategies: Establish clear expectations and clarify purpose | Develop assignment or activity description statements that address use of AI applications in the class. | Set tone and routines early in the term; Engage students in co-designing learning goals, discussing opportunities and limitations of AI, as well as what represents misuse of AI in the course and within specific activities. | Clarify the "why" of each AI-supported activity; Discuss links to key knowledge, skills, and values (see the section below on Transparency). |
Facilitation Strategies: Approach each active learning engagement with care | Consider how you intend to navigate physical and/or virtual learning environments to engage with students about questions of "when," "how," and "why," they are making use of AI in the activity. | Be intentional about creating an inclusive learning environment early in the term; Gauge students’ interests and comfort with using AI; Offer alternatives for participating in AI- supported activities (e.g., provide students AI- generated output instead of requiring use of AI applications). | Model motivation and excitement about AI as a potential learning tool when appropriate; Clarify strategies critical to success; Provide actionable feedback to students. |
A growing body of research suggests several transparency-related elements of instruction are important for enhancing the student experience and supporting academic success. Studies have demonstrated that intentional design of instruction for transparency contributes to greater learning outcome achievement, particularly among first-generation students (Winkelmes et al., 2016; Howard, Winkelmes, and Shegog, 2020) and in teaching large-enrollment classes (Winkelmes, 2013). This notion is further supported by research on the use of explicit assessment criteria, which has been found to support student self-regulation (Balloo et al., 2018).
In approaching the issue of AI in teaching and learning, a few transparency-related considerations are important to note, and include the following:
Strategies | Source | Details |
Transparent, Learning- focused Syllabus Design | Wheeler, Palmer, and Aneece, 2019 | Communicate transparently and explicitly in the syllabus about expectations for students’ course-related uses of AI (See sections on Active Learning and Academic Integrity). |
Assignment and Activity Design for Transparency | Winkelmes et al., 2016 | Apply the Transparency in Learning and Teaching (TILT) to communicate the purpose, task, and criteria for success for each of your course activities, assignments, and assessments. Are AI-related skills relevant? Use this exercise to explicitly state why, when, and how AI may/should be integrated or prohibited (See http://www.tilthighered.com for TILT templates and resources). |
Engage Students in Transparent Practices | Balloo et al., 2018 | Avoid transactional engagements with students in addressing AI (e.g., simply providing policy statements that AI tools may or may not be used to complete class assignments, stating assignment instructions, etc.). Adopt transformative approaches that directly involve the students in review, feedback, and decision-making, particularly around assessment criteria (e.g., a discussion exercise in which students review the grading rubric, ask questions, offer feedback, practice applying it to sample work, and reflect on reasons why the use or exclusion of AI for the assignment might be appropriate). |
A need to address issues and opportunities created by AI that impact teaching and learning is a reality for instructors. The recommendations provided in this document for considering AI and course modifications are informed by evidence-based instructional approaches and strategies. The impact of these changes on student learning and experience, however, will not be immediately clear, and understanding implications of course and instructional redesign efforts related to AI will require intentional steps of evaluation and reflection. Below are a few suggestions to consider for reflecting on teaching, particularly in consideration of AI.
Strategy | Data Source | Details |
Analyze AI-Aligned Summative Assessment Data | Identify summative assessments that align to instruction that leveraged or limited AI. Analyze evidence of student learning. | In revising instruction to embrace, leverage, or prohibit use of AI tools, you are also revising the experience of your students in learning. Summative assessments are designed to measure learning after it has occurred. Therefore, analysis of student performance data generated through summative assessments (e.g., exam questions, essays, projects, presentations) that align to course goals and learning objectives can provide insights into potential implications of the new instructional approach. |
Review Formative Assessment Data Related to AI | Formative assessments include any activities or engagements (e.g., quizzes, in-class activities, student reflections, etc.) that provide a measure of progress in learning while the learning occurs. | A wealth of data is made available through the implementation of formative assessment strategies. Student work that occurs while learning is happening not only supports the learning process but provides valuable information about student progress and the effectiveness of instruction. Taking time to collect, review, and reflect on these sources of data, particularly those connected to potential or realized uses of AI for instruction, can provide you with student-focused insights into their progress and responses to instruction. |
Collect and Consider Mid-Course Student Feedback on Instruction | Small Group Instructional Diagnosis (SGID); Custom surveys of student perceptions. | The Small Group Instructional Diagnosis (SGID) is a service offered by the Drake Institute for Teaching and Learning that provides instructors with valuable student feedback through a focus group-style effort during the term. Information is collected on supports and barriers to student learning, as well as ideas for positive change. A Drake Institute instructional consultant can also inquire about specific elements of instruction, such as AI. Alternatively, instructors can create custom surveys for students, asking for feedback on instructional approaches and perceived barriers or supports to their success. |
Reflect and Consider Your Own Perceptions | Personal reflections on the teaching and learning process, particularly around new uses of AI for teaching and learning. | You can document their own thoughts on the teaching and learning process as it unfolds. Although not recommended as a sole source of data, your own views and observations can serve as a valuable source of information to guide the iterative process of instructional improvement to enhance student learning. |
The ideas presented above are offered in hopes of supporting instructors in navigating through new opportunities and challenges that emerge from increasing access to generative technologies and tools that leverage AI. These approaches are designed to foster motivation, engagement, equity, and academic integrity. However, they in no way eliminate risks of, or opportunities for, unethical and inappropriate student application of AI in course contexts. The Institute encourages instructors to work with the University Committee on Academic Misconduct (COAM)(link is external) to address any instances of suspected misuse that might constitute academic misconduct.
The presence of generative AI tools is and will continue to be a reality that must be acknowledged and addressed with care. The Drake Institute is committed to sharing evidence-based strategies for addressing issues and challenges involved in the teaching and learning process and to working with all who teach and support teaching at Ohio State in advancing instructional excellence.
For questions or to request a consultation around teaching, please visit http://drakeinstitute.osu.edu or e-mail us at drakeinstitute@osu.edu.
Literature Cited:
Andrews, M., Prince, M., Finelli, C., Graham, M., Borrego, M., & Husman, J. (2021) Explanation and facilitation strategies reduce student resistance to active learning. College Teaching, 70(4), 530-540. https://doi.org/10.1080/87567555.2021.1987183(link is external)
Balloo, K., Evans, C., Hughes, A., Zhu, X., & Winstone, N. (2018) Transparency isn't spoon-feeding: How a transformative approach to the use of explicit assessment criteria can support student self-regulation. Frontiers in Education, 3. https://doi.org/10.3389/feduc.2018.00069(link is external)
Bloom, B.S., Engelhart, M. D., Furst, E. J., Hill, W. H., & Krathwohl, D. R. (1956). Taxonomy of educational objectives: The classification of educational goals. Handbook I: Cognitive domain. New York: David McKay Company.
Carvalho, L., Martinez-Maldonado, R., Tsai, Y. S., Markauskaite, L., & de Laat, M. (2022). How can we design for learning in an AI world? Computers and Education: Artificial Intelligence, 3. https://doi.org/10.1016/j.caeai.2022.100053
Fink, D. L., (2013). Creating significant learning experiences: An integrated approach to designing college courses. San Francisco: Jossey-Bass.
Freeman, S., Eddy, S. L., McDonough, M., Smith, M. K., Okoroafor, N., Jordt, H., & Wenderoth, M. P. (2014). Active learning increases student performance in science, engineering, and mathematics. Proceedings of the National Academy of Sciences, 111(23), 8410-8415. https://doi.org/10.1073/pnas.1319030111
Hilton, J. T. (2016). A Case Study of the Application of SAMR and TPACK for Reflection on Technology Integration into Two Social Studies Classrooms. The Social Studies, 107(2), 68–73. https://doi.org/10.1080/00377996.2015.1124376
Howard, T. O., Winkelmes, M., & Shegog, M. (2020) Transparency Teaching in the Virtual Classroom: Assessing the Opportunities and Challenges of Integrating Transparency Teaching Methods with Online Learning, Journal of Political Science Education, 16(2), 198-211, DOI: 10.1080/15512169.2018.1550420
Nguyen, K.A., Borrego, M., Finelli, C.J., DeMonbrun, M., Crockett, C., Tharayil, S., Shekhar, P., Waters, C., & Rosenberg, R. (2021). Instructor strategies to aid implementation of active learning: A systematic literature review. International Journal of STEM Education, 8(1). https://doi.org/10.1186/s40594-021-00270-7(link is external)
Prince, M. (2004), Does Active Learning Work? A Review of the Research. Journal of Engineering Education, 93: 223-231. https://doi.org/10.1002/j.2168-9830.2004.tb00809.x
Theobald, Elli J, Mariah J Hill, Elisa Tran, Sweta Agrawal, E Nicole Arroyo, Shawn Behling, Nyasha Chambwe, et al. 2020. “Active Learning Narrows Achievement Gaps for Underrepresented Students in Undergraduate Science, Technology, Engineering, and Math.” Proc Natl Acad Sci USA 117 (12): 6476. https://doi.org/10.1073/pnas.1916903117
Wheeler, L.B., Palmer, M, & Aneece, I. (2019). Students’ perceptions of course syllabi: The role of syllabi in motivating students. International Journal for the Scholarship of Teaching and Learning, 13(3). https://doi.org/10.20429/ijsotl.2019.130307(link is external)
Wiggins, G., & McTighe, J. (2005). Backward design. In, Understanding by Design (2nd ed., pp. 13-34). Pearson Merrill Prentice Hall.
Winkelmes, M. A. (2013). Transparency in Teaching: Faculty Share Data and Improve Students' Learning. Liberal Education, 99(2).
Winkelmes, M., Bernacki, M., Butler, J., Zochowski, M., Golanics, J., & Weavil, K.H. (2016). A teaching intervention that increases underserved college students’ success. Peer Review, 18(1/2), 31-36.
