Kate Cammack, CfT Co-Director
At a CfT event last spring, four Sewanee faculty shared how they’re using generative AI tools to advance students’ learning. While these activities were originally developed for STEM courses, they offer valuable examples of ways that any of us, regardless of discipline, might use AI to support our teaching. We’d like to provide examples from a variety of disciplines in our future posts; if you’d like to share your own example, please let us know here.
Clint Smith (Biology) developed an activity that provided a framework for students to study/practice course content at higher levels of Bloom’s taxonomy.
- Goals: (a) help students engage in reviewing course content at deeper, more advanced levels, and (b) reduce the equity gap in students’ study skills.
- Course: a 300-level biology course (BIOL340 Microbiology)
- What he did: Students asked AI to use Socratic questioning to test their content knowledge and build metacognitive skills. Here is his activity (image of first page is below).
- Also: Clint underscored the pedagogical value of building in regular opportunities for metacognitive reflection.
Ali Raeber (Chemistry) developed an activity that built critical thinking skills around online information.
- Goals: (a) help students evaluate scientific courses and (b) learn how to cite those sources
- Course: a 100-level chemistry course (CHEM120 General Chemistry)
What she did: Briefly, students googled a question, read the AI overview of the response, looked for the sources, then evaluated the validity and quality of the sources (e.g., looked for scientific citations).
- Also: Ali has also helped students prepare for exams by critiquing AI-generated questions & responses, using a chatbot.
Teri Terigele (Psychology) developed an activity that enabled students to apply their course knowledge to critique AI output.
- Goals: (a) help students critique AI-generated advice and (b) identify cultural biases embedded in AI algorithms.
- Course: a 200-level psychology course (PSYC229 Cross-Cultural Psychology)
- What she did: Students applied their course knowledge to create a “Cultural Survival Guide,” then asked AI to do the same. They compared/contrasted these guides, with a particular focus on culturally relevant language and content. In a separate activity, students asked AI to generate images of various professions, then discussed how these AI-generated images perpetuated or challenged stereotypes, as well as the (in)visibility of people across different genders, socio-economic statuses, ages, and (dis)abilities.
- Also: Teri introduced a few theories (e.g., this) that described how people adopt new technologies, and also some relevant psychological constructs.
Ross Sowell (Computer Science) developed an activity that explores the capabilities and limitations of AI to underscore key challenges in programming intelligent systems.
- Goals: (a) help students identify limitations of AI and (b) think creatively about how to work around those limitations
- Course: various computer science courses
- What he did: Students explored how well an algorithm could reflect a complex process (object recognition and robot navigation). Students discovered that this process involved multiple steps (e.g., sensing the environment, planning an action, performing the action, and re-sensing the environment) and that each required the programmer to make numerous choices and decisions.
- Also: Ross mentioned efforts to scaffold the introduction of generative AI across departmental curricula are underway at various small liberal arts colleges (SLACs)
Read an article or book about AI & teaching that might be worth sharing with colleagues? Have a new insight or hear something helpful that others might benefit from? Please let us know here.