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Visit this space often for tips and suggestions for teaching in the AI Context.
Explore how AI can support student thinking, redesign assignments for a learning environment where AI is present, and adapt assessment approaches to emphasize process, judgment, and reflection. At this time, there are two approaches that Centre is taking:
Engaging AI thoughtfully with one’s teaching and everyday technology practices. Aimed at faculty who are already experimenting, sampling, and/or curious about AI tools, this approach involves exchange of ideas and supports curiosity and confidence. (Flagged in Blue)
Mitigating AI abuse through thoughtful teaching and learning practices. AI challenges traditional assumptions about learning, authorship, and academic integrity. This approach is aimed at faculty who are concerned about the future, this approach fosters conversation about adaptation and AI literacy to cultivate confidence and capacity to foster deep learning. (Flagged in Green)
Incorporate Process Writing
Process writing deepens conceptual learning by asking students to reflect on their writing process and decisions.
Need ideas for prompts? Contact Nisha.
Safe AI Experimentation
The CTL has a premium ChatGPT account available for faculty experimentation where data entered is not used to train the AI model.
Teaching Students Prompt Crafting
From Elon University’s Student Guide to Artificial Intelligence:
Use correct spelling and grammar.
Be clear, specific, and detailed.
Provide context and perspective.
Break complex tasks into multiple prompts.
Specify format, tone, and style.
Have Students slow down their reading
AI may easily produce reading summaries and “chatting with a text” which may help students initially encounter a difficult text, but it also may offer an illusory mastery of the material if used uncritically. Encourage students to slow down their reading of critical texts in your discipline by engaging with them meaningfully. Here are three ideas to “slow down reading”.
Use annotation assignments. Perusall is a tool available to Centre faculty via Moodle. Students read and view course materials in a collaborative online platform where they ask questions and discuss content.
Shared documents. Have students record significant quotes and interact with one another in shared google or onedrive documents about course materials and assignments or prepping for exams.
Concept maps. Through a graphic representation students create diagrams that show the relationships between ideas, with connecting lines and linking words visually and textually describing the relationships between concepts.
What is a ChatBot? What is Conversational AI? What is an agent?
A chatbot, or bot, is programmed to look for specific text or voice input and reply with preset answers. Rule-based chatbots are a much older form of conversational support, relying on keyword-based if/then logic and structured scripts.
A Conversational AI does a lot more than recognize keywords. It relies on natural language processing (NLP), machine learning (ML), and natural language understanding (NLU) to understand what a customer’s asking, even when they phrase something differently than expected. A chatbot can answer a question. A conversational AI assistant doesn’t just hold a conversation, it can also understand and adapt to resolve complex problems.
Generative AI grew out of machine learning advances focused on pattern recognition and content creation. GAI produces text, code, or images when asked but doesn’t make independent choices.
Agentic AI goes to the next level. Agentic AI adds something new: initiative. It doesn’t just respond. It chooses, plans, and executes. An agent uses tools (search, email, slack, or whatever you can imagine) to accomplish a real task requested by the user. Agents know when to call what tools, in what order, until the job is done.
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