The highlighter may be the unofficial symbol of studying, but should it be? Highlighting and re-reading are generally poor ways for us to retain and apply what we learn (Dunlosky et al., 2013), but old habits are hard to shake. Early in the semester or before larger assessments, prompt students to think about how they study and whether they should reconsider their approach. This teaching tip provides a low-prep way for students to compare their study habits to what research says are among the most effective learning behaviors.

How Students Can Evaluate Their Studying

Have students identify their next reading assignment.

Have them identify what they need to read, by when, and for what purpose (for more on purpose, see Reading Outcomes Promotes Transparency Teaching Tip). This is a great way to (re)engage students with the syllabus and have them start planning ahead. Make sure you have located this first reading assignment as well!

Ask students to write down how they would study the content they read.

• When will they begin the read?
• How often will they read and study the concepts?
• What do they do?
• What does studying look like to them?
• Give them three minutes to respond in writing in phrases and lists as relevant, and encourage them to be as specific (and honest) as possible. 

Have students match what they wrote to a list of study behaviors.

Display these 10 study behaviors, and ask students to label phrases with the corresponding behavior(s). After 1-2 minutes, ask them which behaviors they used the most and which they rarely, if ever, used.

1. Distributed Practice: Implementing a schedule of practice that spreads out study activities over time
2. Elaborative Interrogation: Explaining in your own words why a concept is true
3. Highlighting/Underlining: Marking potentially important portions of materials during the initial reading
4. Imagery used for text learning: Forming mental images of text materials while reading or listening.
5. Interleaved Practice: Creating a schedule of study that combines interactive studying (via practice quizzes, self teaching, etc.) with review studying (flashcards, study guides) within a single study session
6. Keyword Mnemonic: Using keywords and mental imagery to associate verbal materials
7. Practice Testing: Taking practice tests to help learn the material
8. Rereading: Reading the text material again after initial familiarization
9. Self-Explanation: Describing how new information is related to known information, or explaining steps taken during problem solving
10. Summarization: Writing summaries (of various lengths) of to-be-learned texts

Show students these study behaviors in order of effectiveness.

Explain that these study strategies have been studied in terms of how well students remembered what they learned based on the strategy they used (Dunlosky et al., 2013). Show students the results, as displayed below. Those who employed practice testing and distributed testing best retained and applied what they learned, while those who employed common strategies like rereading and highlighting gained the least from their study efforts. 

The Most Effective

• G: Practice Testing - Taking practice tests to help learn the material 
• A: Distributed Practice - Implementing a schedule of practice that spreads out study activities over time

Moderately Effective

• B: Elaborative Interrogation - Explaining in your own words why a concept is true 
• I: Self-Explanation - Describing how new information is related to known information, or explaining steps taken during problem solving
• E: Interleaved Practice - Creating a schedule of study that combines interactive studying (via practice quizzes, self teaching, etc.) with review studying (flashcards, study guides) within a single study session

Least Effective

• H: Rereading - Reading the text material again after initial familiarization 
• J: Summarization - Writing summaries (of various lengths) of to-be-learned texts
• C: Highlighting/underlining - Marking potentially important portions of materials during the initial reading
• F: Keyword Mnemonic - Using keywords and mental imagery to associate verbal materials
• D: Imagery used for text learning - Forming mental images of text materials while reading or listening. 

Discuss this list, and how students might revise their study strategies.

Based on your discipline, in-class activities, and other considerations, encourage students to think critically about these study methods and their own study methods. Is a serial highlighter who consistently gets high marks proof that these findings are wrong? What does “practice testing” look like with your textbooks, readings, and other learning materials? Then, invite students to reflect on their initial response and consider what study strategies they might try out. 

References and Resources

Dunlosky J., Rawson, K., Marsh, E., Nathan, M. J., & Willingham, D. (2013). Improving students’ learning with effective learning techniques: Promising directions from cognitive and educational psychology. Psychological Science in the Public Interest, 14(1), 4-­58. 

How Can We Convince Students That Easier Doesn’t Always Mean Better? This Chronicle of Higher Education article explains the concepts presented in this teaching tip and links to helpful student resources on learning, such as those from The Learning Scientists.

In OU professor Barbara Oakley’s renowned Learning How to Learn MOOC, she describes many practices that puts into effect these study strategies, such as interleaving, recall, and distributed practice.

Saundra McGuire’s Teach Students How to Learn (2015) provides a deep but accessible dive into how students can better grasp and apply what they learn. Available in the CETL Library.

Written by Christina Moore, Center for Excellence in Teaching and Learning at Oakland University. Originally published September 3, 2019. Others may share and adapt under Creative Commons License CC BY-NC.

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This teaching tip has been adapted from materials developed by Sarah Hosch and Daniel Brengel as a part of the Critical Thinking and AI Course Development Institute.

How we engage with generative AI tools determines the value of any output – or as my mother always said, “garbage in, garbage out.” Effective, critical, and intentional engagement with tools like ChatGPT, Gemini, or Claude begins with prompting, or writing clear, purposeful instructions for generative AI tools with the eventual output in mind. 

It’s useful for all faculty to have a working knowledge of effective prompting (sometimes called prompt engineering), regardless of our philosophies or policies around AI use in our classrooms. For those looking to engage with generative AI tools to support their instructional work, they may find that well-structured and well-designed prompts offer pathways for reconsidering course design, learning activities, and assessment. For those seeking to avoid AI use in their learning environments, effective prompting can help to stress-test assignments or assessments (as in, how easily can learners offload class activities to generative AI tools). Effective prompting can be used to critically analyze output that comes from these generative AI tools, useful across AI stances. 

Prompting to Support Teaching & Learning

In The Science of Learning Meets AI, Lewis D. Ludwig and Todd D. Zakrajsek (2026) offer a suggested prompt for faculty looking to use AI tools to positively impact teaching and learning in their classrooms (p. 54):

I teach [insert course name] to [insert number of students] in [insert discipline]. Help me create a genAI prompt for this teaching goal: [Paste example of genAI use in this stage here]. Give me a complete, ready to use prompt I can enter into genAI, along with instructions on how to use it.

This prompt illustrates three core factors of effective prompts known as ACT (act, context/constraints, task):

• we need to instruct generative AI tools what to act as (i.e., "Act as an expert instructional designer specializing in higher education."), 
• the context and constraints that need to guide any output (e.g. a specific discipline, group of students, course format, instructional goals, or teaching priorities), and 
• the task we want these tools to produce in concrete terms.

Concrete Starting Points with the AI Prompt Studio

As faculty consider whether it might be helpful to prompt a generative AI tool in support of teaching and learning in their classrooms, they may want to explore the structured options in the AI Prompt Studio. CETL’s AI Prompt Studio provides starting points as faculty hone these skills in support of teaching and learning work such as course design, student communication, learning engagement, and assessment. For example:

I am running a course for [student level] students at a university. What are some icebreaker questions or activities that can be used for [course or small group] discussion?

Here are some concrete examples of how I’ve used effective prompting to meaningfully impact instructional interactions:

• For learner engagement: 
• I needed to clarify and condense a video lecture – so I uploaded the transcript into Gemini and prompted the tool to consolidate that content into a revised script I could use to re-record this lecture. 
• I stipulated that the resulting resource needed to avoid jargon while still addressing all of the key ideas in the transcript, and that the language should be at the level of a first- or second-year college student. 
• Gemini provided a clearer revised script, which I was then able to compare to my lecture materials. 
• While some important ideas were missing or had been overly simplified from the generative AI tool’s output, this prompt helped me get a clearer starting point that I could fill gaps and flesh out, while still being more concise and straightforward. 
• For instructional assessment: 
• I needed to develop an online question bank for a learning assessment. 
• I uploaded existing questions I’d written into Gemini Notebook and prompted the tool to write 4-5 different versions of the items, at the level of a first- or second-year college student, that were scenario-based. 
• Moreover, I needed a mix of question types (multiple choice, true/false, short answer, and matching). 
• The resulting output provided me with a starting point I could review, edit, and hone; I also found myself re-prompting Gemini Notebook to simplify or clarify questions. 
• And, what’s more – I could then prompt the tool to format the questions so I could directly upload them to Moodle.

Both of these instances involved iterating, revising, and asking for different options, which are hallmarks of effective prompting. I reviewed and checked all AI-informed content before I used it, because not everything was accurate, appropriate, or aligned with my goals. And, critically, my own faculty expertise was still indispensable alongside the technical support of these tools – and that’s the case in any scenario in which we prompt generative AI tools to support our teaching and learning!

Considering Prompting in Your Practice

As faculty explore effective prompting in their work, it may be helpful to begin by considering the following questions:

• How does using generative AI tools in this way align with, or diverge from, my values around using AI?
• What areas of my teaching and learning could use additional clarity, structure, or options? Could I prompt AI tools to help me develop those areas? 
• If I use generative AI in this way, how will I acknowledge it in my course documents?
• Could generative AI tools help me to better align module or lesson outcomes with broader course goals? If so, how? If not, why?
• Could generative AI tools help me to develop assignments and assessments for my courses? If so, in what ways? If not, why?
• How could generative AI tools help me to create clearer rubrics and feedback language for learning assessment?

In considering these questions, it’s vital that we keep our faculty expertise, disciplinary knowledge, and pedagogical judgment at the center of all decision-making. When we do so, we ensure that effective prompting is a tool we can leverage in meaningful and thoughtful ways in our practice, regardless of our position on AI use in learning.

References and Resources

Ludwig, L. D., & Zakrajsek, T. D. (2026). The science of learning meets AI: A practical faculty guide to purposeful integration, student engagement, and ethical practice. Routledge.

About the Author

Amanda Nichols Hess is a Professor and the Coordinator of Instruction and Research Help in the Kresge Library. This year, she is also serving as the CETL Faculty Fellow for AI. Her research focuses on information literacy instruction, faculty development, online learning, and how these concepts intersect. Outside of work, Amanda moonlights as her children’s elementary school mascot (which is a Dragon).

Others may share and adapt under Creative Commons License CC BY-NC.

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