Effective TEaching AND Generative AI
Note: We recommend consulting this resource alongside the campus guidelines for using generative artificial intelligence at Mines.
Effective Teaching describes teaching that is intentionally designed, focused on learning, supportive of students, and reflective. The continued development of ChatGPT and generative AI (genAI) offers us an opportunity to revisit our instructional methods and course designs to ensure that we are upholding these four characteristics. This resource provides specific recommendations, strategies, and resources for implementing effective teaching methods within the contexts of ever-evolving artificial intelligence models and tools. When considering how you will approach genAI in your classroom, you might start by reflecting on the following questions:
How will you be intentional about the use of genAI in the classroom?
When will you encourage it or prohibit it, and why?
How will you communicate your intentions to students?
Focused on Learning
What new skills and knowledge do your students need given the emergence of genAI in both the workplace and at home?
How might genAI be used to enhance student learning and metacognitive thinking in your course?
Supportive of Students
How will you move away from a punitive culture to a culture of support and trust with students?
How will you ensure equitable access to genAI tools in your classroom?
What will you do to continue learning (and sharing ideas) about effective teaching and genAI?
How will you collect and implement student feedback about your approach to genAI?
As you reflect on these questions, we encourage you to:
- Approach ChatGPT and genAI as tools that—like calculators, statistical analysis and design software, the internet, and so forth—can both interfere with or deeply enhance and enrich the learning process.
- Approach AI and digital literacies as fundamental skills that students will need in their respective fields and as public citizens and consumers. From this perspective, completely “banning” genAI from a course or ignoring it altogether can deny your students an important opportunity to critically engage with cutting-edge tools that are actively impacting the world around them.
- Shift from a CHEATing approach to a TEACHing approach: Rather than relying on a punitive culture or over-utilizing problematic detection tools to police students, embrace genAI as an opportunity to learn alongside students while addressing the root causes of cheating. We know, for instance, that students are more likely to cheat when:
- They don’t see the personal or social relevance of course tasks (coursework feels like “busy work”).
- They believe that making a mistake constitutes failure.
- They feel unsupported as learners and as people.
- They feel that their contributions don’t matter to the instructor or to their peers.
- They are provided with only one or two high-stakes opportunities to demonstrate their knowledge (in one format).
- There is an emphasis on the extrinsic motivation to earn (points) rather than an intrinsic motivation to learn.
We recommend a proactive, holistic approach that disincentivizes cheating by building trust and community with students, interrogating the ethics of genAI, ensuring equitable access, explicitly communicating expectations and relevance of course tasks, and (re)designing assignments TO center the human in learning by tapping into student motivation, iteration, agency, lived experience, and creativity.
Broad Strategies for effective TEACHing with genAI:
Explicitly tell students that you trust them, care about their learning and about them as people, are excited to learn from them, and are there to help them succeed.
Ask students to describe and reflect on how they use genAI on individual assignments and in their everyday lives.
Bring students into the decision-making process when developing classroom norms around genAI use.
Provide ways for students to work collaboratively, learn from one another, and reflect on their process.
Be transparent about how you use (and don’t use) genAI as an instructor.
Be vulnerable by sharing your mistakes and struggles.
Be open to making course adjustments based on student feedback.
Invite students to analyze and discuss ethical and unethical uses of genAI (both in society and in the course). This might also include conversations about the ethical considerations of instructors using genAI to grade, generate lesson plans, and so forth.
Develop accessible and equitable course policies related to genAI tools, knowing that not all genAI models are free, and the most updated versions come at a cost.
Invite students to crowd-source free and accessible tools that they use. Discuss these tools together as a class and set parameters around genAI use.
Develop a shared literacy around genAI by teaching students how to navigate these tools prior to expecting significant use in the classroom.
Don’t try to prevent genAI use by requiring handwritten submissions only, as this builds unnecessary barriers for folks with disabilities.
Don’t try to prevent genAI use by switching exclusively to timed in-class tests, as this builds barriers for students with disabilities, students from underrepresented backgrounds, non-native English speakers, and so forth.
Proactively investigate the accessibility of genAI tools. Submit a request to ITS for assistance.
Design assignments with multiple and various options. Ensure that assignment requirements and components do not undermine any approved accomodations.
Set (or co-create) clear expectations with students about how, when, and why they are encouraged (and discouraged) to use genAI in your course. Discuss this in class and provide these expectations in writing on your course syllabus.
Communicate the social importance and relevance of your course, both in terms of your course topics and the skills that students will develop throughout the term.
Spark creativity and intrinsic motivation through authentic assignments that encourage student choice and multimodality.
Re-design assignments that are easily completed by genAI tools.
Assess the iterative learning process as much as (if not more than) the output.
Foster metacognitive thinking skills by asking students to identify where genAI complements, supplants, or fails to replace human contributions.
Normalize making mistakes by offering low-stakes practice with feedback and reflection.
Connect course content with student interests, lived experiences, and real-world examples.
Reward growth through multiple revision opportunities.
Ideas for (RE)Designing Assignments That Encourage Critical Engagement with Generative AI
Ask students to practice formulating effective prompts around a common topic, using AI-generated text or images to brainstorm new designs and ideas. This could be an individual assignment or a collaborative activity where students share and compare their outputs using various phrases and commands. Invite students to reflect on their experience and identify strategies they took to generate useful prompts.
Ask students to critically evaluate AI-generated outputs and their limitations with respect to accuracy, persuasiveness, bias, equity, quality, and so forth. This may include evaluating genAI outputs with a rubric, fact-checking genAI outputs using scholarly sources, investigating genAI companies and algorithms to identify biases, inequities, or privacy concerns; critiquing genAI outputs in an original argument, or editing genAI content to improve it.
Ask students to keep a reflective journal to describe how they utilized genAI for any given assignment or project—what they learned by using it, what challenges and frustrations they faced, what they did to overcome those challenges, their takeaways from the experience, how AI-generated feedback differed from instructor feedback, etc.
Structure multi-stage, open-ended group projects that invite students create something and/or innovate using genAI. Encourage community-based assessment of these projects so that students can share their innovations with one another. Include a reflection that asks students to evaluate their own project management strategies and to consider the critical and ethical choices they made while using genAI throughout the project.
Ask students to compare their own work with AI-generated work. How does their work improve upon genAI content? What strengths, tendencies, and perspectives do they bring to the work? What might they change about their own work given genAI outputs?
Bring in guest speakers from your field or industry to discuss how genAI is impacting the workplace.
Ideas for (Re)designing Assignments To be More Human- (Learner-) Centered
Increase the specificity of your questions and tasks. Incorporate class conversations, case studies, course readings, and personal experience into your question prompts. Ask students to cite the textbook or other readings in class. Not only will this create tasks that are more difficult for genAI; it will also help students to strengthen their contextual understanding of course content.
Reward iterative learning and growth through multi-stage assignments, revisions, and reflections. Encourage students to submit works-in-progress (reports, drafts, reflections, etc.) for low-stakes feedback. Throughout the term, structure student-student and student-instructor feedback loops so that students can receive insights and constructive criticism from both their peers and the instructor. Consider alternative assessments and grading policies that decenter the focus on external motivation (getting points and grades) and reward student perseverance and growth.
Provide students with an opportunity to share original work to a public-facing audience or their peers. Tapping into the social dimension of learning can help foster intrinsic motivation and encourage students to take ownership over their own work.
Add more opportunities for students to explain their reasoning (what they did and why they did it). You might add a space next to an exam question for students to explain their process; a reflective writing assignment where students can explain revisions they made on a previous draft; or a group activity where students evaluate a case study and justify their proposed solution to their peers.
Offer students more choices about their learning experience. You can foster intrinsic motivation by providing a few options for assignments or readings. You might assign three readings and allow students to select two of their choice; provide two prompts and invite students to choose which one to answer; ask students to communicate in a medium of their choice; or ask students to identify their own personal learning goals throughout the term.
- Chat GPT Advice Academics Can Use Now (Inside Higher Ed)
- Instructor Guide for Responding to Generative AI (George Washington University)
- Artificial Intelligence Enhances Learning in the Architecture Classroom (George Washington University)
- AI Text Generators: Resource Page (George Mason University)
- Chat GPT and Other AI Tools: Implications for Teaching and Learning (Oregon State University)
- Critical AI: Some Next Steps For Educators (CriticalAI.Org)
- How to Cite ChatGPT (APA)
- Artificial Intelligence and the Future of Teaching and Learning (U.S. Department of Education)