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Instruction Sessions


Image informed by Deakin Library from "Research Data Lifecycle" by LMA Research Data Management Working Group is licensed under CC BY-NC 4.0

  • What is AI/Demystifying

  • Critical thinking - research (how to think critically when using AI)

    • What's included/not included? (table below)

    • Example for statistics - shows which data sets to check, but inaccurate (example pictures below)

  • Ethics

    • Checklist for Responsible AI Use

      • Evaluate Suitability - Ensure AI first the task and doesn't reinforce harmful biases

      • Get Approval - Check for consent (syllabus, journal submission details, assignment description, company policy) before using AI on projects

      • Consider Sustainability - Consider the environmental impact and if AI is the best tool for the task

      • Protect Privacy - Use secure tools and avoid sharing sensitive or personal data

      • Validate Outputs - Review all AI-generated content before sharing

      • Be Transparent - Disclose AI use to teams, instructors, students, and clients

    • privacy (free = you are the product)

    • accountability

    • AI pause flowchart https://library.bu.edu/ld.php?content_id=81961548 

  • Tools

  • Lesson plans

  • Activities

AI Assignment Library (University of North Dakota): https://commons.und.edu/ai-assignment-library/


  • Critical thinking - Prompt engineering

    • A prompt is the human-created input an AI model uses to generate an output.

    • Integrating prompt engineering techniques when using an AI chatbot tool (such as ChatGPT, Claude, Copilot, Gemeni, etc.) can improve outputs because well-crafted prompts yield more accurate, relevant, and useful AI-generated responses. As a bonus, writing effective prompts requires clarity, specificity, and an understanding of how AI processes language, which strengthens your critical thinking, analytical, and communication skills. It must be stated, however, that the discourse about the effectiveness of prompt engineering, like AI technology itself, is rapidly changing to the point that some question the future usefulness of prompt engineering. 

    • When teaching prompt engineering skills, best practices suggest there are a few components to a prompt deemed somewhere between essential (TASK) and important (CONTEXT, EXAMPLE), and a few components to a prompt that one could classify as useful but not mandatory (PERSONA, FORMAT, TONE). 

      • Essential = Task (what you want the AI chatbot to do)

      • Important = Context (give the chatbot enough information to constrain the endless possible responses); and Example(s) (including a relevant example greatly improves the quality of the output).

      • Useful, NOT Mandatory: Persona (give the AI a role to embody for more specific results, but remember that it helps if the persona is someone well-known); Format (visualize what you want the chatbot to create; bullet points, charts, graphs, paragraphs, etc? Give it instructions to limit the options); Tone (options could include casual, formal, pessimistic, etc.; you could select a "feeling," prompt the chatbot for possible tone keywords you might choose from, or check out this table of tone descriptors at the bottom of the page)

      • Video examples of these prompt engineering skills in action can be found here and here. Examples of these techniques can be found on the guides here and here.

      • Another example of a prompt engineering framework often referenced in LIS circles includes the acronym CLEAR (Concise, Logical, Explicit, Adaptive, Reflective), popularized by Lo, L. S. (2023). The CLEAR path: A framework for enhancing information literacy through prompt engineering. The Journal of Academic Librarianship, 49(4). https://doi.org/10.1016/j.acalib.2023.102720. 


    • Better Prompting_crop.jpg

    "Getting Started with prompting" from Writing Prompts, a section of AI in Education by University of Sydney used under a CC-BY-NC license.



    TEST


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