Designing Courses in the Age of AI
Generative AI tools add yet another disruption to established teaching practices. In the face of tools that can replicate or mimic much of the work we ask our students to do in higher education, we need to reflect deeply about our role in educating students and our own professional standards. However, instructors can begin address these new challenges with course-design strategies and approaches.
Large Language Models (LLMs) such as OpenAI’s ChatGPT, Google’s Gemini or Anthropic’s Claude will generate written work in a range of writing styles, revise written content, solve problems and respond most any prompt or question. AI models can also write computer code, analyze data, generate images, presentations or even videos. These systems do not always create high quality output, and some of the content they generate may include incorrect information, biased content, plagiarized content, and fabricated references. Nonetheless, their capacities are rapidly evolving and becoming more sophisticated. Moreover, they are now being integrated into everyday applications students use to complete course work (e.g., Google Documents, or iPad Calculators). While there are some common linguistic features faculty notice when AI is naively asked to generate an assignment, most well-designed-interactions with AI are not easily or accurately detectable.
Some instructors are asking how these systems change what students need and want to learn, others are exploring ways that AI can enhance students’ learning, and all of us are considering how to adapt our individual courses and in particular our assessments. Below are suggestions to assist instructors in prioritizing your efforts as you explore the impacts of AI on teaching.
Communicate with your students about AI policies and expectations in your course
While some instructors may encourage students to use AI tools, others may restrict their use or forbid them entirely. It’s critical, therefore, to be upfront and clear about how you expect students to interact with AI tools in your course and in each of your assignment. Students are already required to adapt to a variety of academic integrity policies in each of their courses, since expectations for collaboration, citations and tool use vary class by class. Clarity about appropriate and inappropriate uses of AI at the syllabus and assignment level help students navigate the widely disparate perspectives they are encountering.
Tufts’ School of AS&E has a set of Academic Integrity Resources which can be helpful in thinking through what policies you would like to state explicitly in your class. You can also engage students in conversations about AI collaboratively considering questions such as – How do they see education evolving in the face of AI? What do they believe are ethical concerns or appropriate uses of AI in the context of this course? How can AI be used to enhance their learning and what uses of AI might interrupt the effort and time required to develop deeper understandings and acquire new skills?
For Tufts examples and guidance to help you get started writing your statement, visit Developing Syllabus Statements for AI (CELT)
Revise course assignments to minimize the value of outsourcing thinking to an AI
Here are some approaches that can both promote deeper learning experiences for students and dissuade their use of shortcuts such as AI tools in assignments. For more ideas see Part 1 of the series Addressing Academic Integrity in the Age of AI , “Beyond AI Detection – Rethinking Academic Assessments and Part 2: The AI Marble Layer Cake – Reconsidering In-Class and Out-of-Class Learning & Assessment.
Emphasize process over product
The first strategy is to emphasize process-oriented tasks (instead of product oriented ones) . Asking students to revise their ideas iteratively, through various stages, can help make explicit important skills such as how to analyze sources of information, revise writing to target different audiences, evaluate their own work and incorporate feedback to improve it.
Offer choice and personalization
A second approach targets increasing student motivation through choice and personalization. Students are less likely to want to use outside tools when able to explore ideas that are personally meaningful to them (for example interviewing a family member), learn things that they believe are important to their future goals, and create products of work that are fun.
Promote social learning
The third complementary approach engages students in social learning (e.g., discussing ideas with peers, collaboratively synthesizing information, clarifying misconceptions or working together to solve problems). In these scenarios it’s more difficult for students to employ AI tools to participate on their behalf.
Have your students reflective on their learning process
Reflections ask students articulate what and how they are learning providing them the opportunity to see the value in the effortful and timely tasks of learning.
- What are we learning and why is it important?
- What background knowledge and skills did I assume students would bring?
- Have I asked students to reflect on: 1. What am I learning right now? 2. How is it going? 3. Where do I want to go next in my learning?
for more see Metacognitive Prompts (TeachThought)
Become Familiar with Generative AI
While the pace at which some students are adopting AI, is exceeding that of many instructors, both instructors and students across all disciplines at Tufts need to acquire critical literacy in the use of AI, which includes understanding how it works, and how to use it effectively and responsibly.
How might an instructor get started? One of the best ways to learn about AI systems is through hands on experience – keeping in mind not only our courses, but also our professional work and broader lives. Understanding these systems can take a few hours of play, informed by some effective prompt strategies (e.g., the Prompt Library from More Useful Things). Instructors can ask AI to revise their writing, suggest course activities or exam questions, and see what different Generative AI systems will return when asked to complete the assignments you give your students. Explore how different systems and different prompts will generate results of varying quality. See TTS’s site Generative AI Tools for suggestions to get started. As you play with these systems in professional and educational contexts, pause and ask how might AI change (or not) some of the skills and knowledge students will need in their future careers.
Consider Ways AI Tools Could Enhance Your Course
While it’s important to be cautious about the ethics of asking students to create accounts with AI platforms (see e.g., ChatGPT and Good Intentions in Higher Ed), there are a variety of ways these tools might enhance a students learning within a course. For example, a generative AI system such as ChatGPT can provide feedback on a student’s writing (or serve as a brainstorming partner). AIs can be used for personal tutoring and to answer questions or explain confusing concepts in multiple ways, with the understanding that sometimes the information provided may be inaccurate or biased. For more ideas about how students might engage with AI within a course see consider this worksheet with an example list of expectations and this AI Assignment Scale. Students can also learn by exploring these tools themselves, looking for factual errors in AI generated responses, considering differences in responses arising from different tools, comparing AI generated works to ones from the literature or their own work.
For more examples see:
- Incorporating AI in Teaching: Practical Examples for Busy Instructors by Daniel Stanford, July 2023
- ChatGPT in Veterinary Medicine: A Practical Guidance of Generative Artificial Intelligence in Clinics, Education, and Research by Candice Chu, Feb 2024
- How to Use GPT-4-o Voice in the University EFL Classroom by Richard Campbell, May 2024
- GenAI Chatbot Prompt Library for Educators from AI for Education
- AI Prompts for Teaching by Cynthia Alby
The SPACE Framework: Teaching Students to Write with AI
- Set directions for how students engage AI systems
- Prompt AI to provide specific outputs
- Assess the AI output to check for accuracy, bias and writing quality/voice
- Curate AI-generated text from multiple sources, combined with human contributions
- Edit to integrate AI & human created content
for more see Teaching Students to Write with AI: The SPACE Framework by Glenn Kleiman
Begin Conversations About What These Tools Might Mean for Tufts and Higher Education
Engaging around this issue with faculty colleagues, staff and students will enhance the conversation, improve our understanding, and lead to shared approaches for how we think about new AI tools and how we will manage them. Explore CELT’s collection of Artificial Intelligence resources as you consider how to engage in dialogue about the implications of AI at different levels. We also must begin the conversations about what Responsible Use of AI in Higher Education looks like within each of our contexts.
CELT & ETS are also here to support you. Reach out for an individual consultation to talk about specific concerns and strategies in your courses —
Learn More:
- Teaching and Generative AI: Pedagogical Possibilities and Productive Tensions eds Beth Buyserie, & Travis N. Thurston
- Generative AI and the Problem of (Dis)Trust by J Riyeff in Inside Higher Ed. June 2024
- Instructors as Innovators: A future-focused approach to new AI learning opportunities, with prompts by E.R. Mollick & L. Mollick, April 2024
- The Program-Level AI Conversations We Should Be Having by K. Labdy in Inside Higher Ed. Feb 2024
- Generative AI and Creative Learning: Concerns, Opportunities, and Choices An MIT Exploration of Generative AI by Mitchel Resnick March, 2024
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