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When are the sessions going to be?

Where will they be?

Advertising

What kind of students might we expect?

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Gen AI concerns

Ethical use

Equitable access

Climate

How to use AI effectively/get the most out of it

Referencing and acknowledging

Where to start with AI

Fear around using AI

Copyright and security of content – looking after both personal and university IP

Prompting

Using AI as a critical friend

Why use co-pilot (could come under ethical use)

Proof reading

Problem solving with AI

Answering assignment questions with AI

Possible uses of AI

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Ohio State University published a paper on their adaption of Bloom's taxonomy for information literacy. It can easily be mapped to AI literacy also:

Remember – general awareness and knowledge of skills; able to name AI tools and their functions. Basic understanding of the pros and cons – generic and in the academic field (academic integrity impact).

Understand – basic skills with using AI, understands how to write a prompt and how to improve responses and interrogate the results. Knows where to find the AI tools.

Apply – Able to apply their knowledge and skills in their use of the tools and using more advanced skills e.g. lateral reading for evaluation, checking multiple tools against others.

Analyse - able to pick the right tool for the goal outcome and put the previous steps into specific scenarios. Confidence in using the tools. Arriving at own conclusions about what is suitable to use, asking discernible questions, going beyond the tools to the develop research skills further.

Create – using the tools within assignments appropriately, able to advise others on the use of AI; understanding how AI fits into the wider digital and information literacy landscape.

Adapted from Folk et al. (2024) ‘Building a bridge between skills and thresholds’, Journal of Information Literacy, 18(1). Available at: https://doi.org/10.11645/18.1.58

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Personal critical framework for using Gen AI.

Skills benchmark (can I do this myself?)

The issue of good academic style and Gen AI as an editing tool.

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The pedagogical approach recognises that true competence emerges not from knowing facts about GenAI capabilities, but from demonstrating the practical wisdom to engage critically with these tools and the content generated.

Reflecting the ideas shared here, the students would practice the art of iterative questioning, learning to push back against responses, challenge underlying assumptions, and guide GenAI systems toward more nuanced understanding. This controlled practice environment allows for the kind of deliberate knowledge, skills and attitudes development that Miller's framework advocates for, moving beyond theoretical knowledge toward demonstrable competency.

The progression embedded within the workshops would reflect a journey from novice understanding of GenAI dimensions toward expert-level discernment. Through repeated practice in controlled scenarios, participants develop the confidence to treat GenAI as a 'collaborative' partner rather than an authority, cultivating the kind of critical collaboration skills that will serve them throughout their academic and professional work.

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I've been thinking about how we might frame these workshops around Miller's Pyramid - positioning students in the 'Shows How' domain where they can demonstrate developing expertise through controlled practice scenarios. Rather than treating GenAI as just a productivity tool, what if we focused on helping students cultivate sophisticated dialogues with these systems through structured exercises that mirror authentic academic and professional contexts?

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Mastering GenAI: Your Personal Development Roadmap @Bath | Academic & Employability Skills

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Before we dive in, let’s talk about the framework behind this guide. We’re using Miller’s Pyramid, a groundbreaking model originally developed by George Miller in 1990 for medical education. Developing knowledge and competencies in new technologies can be challenging, which is why this powerful framework has been adapted here to help you build structured knowledge, skills and attitudes to confidently navigate the world of Generative Artificial Intelligence (GenAI).

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Tasks for Research:

Assisting with literature search

Identifying keywords from assignment questions, abstract

Recommending search strings

Generating related terms

Exploring Elicit, Research Rabbit

Tasks for Feedback:

'Polishing' writing or changing tone

Checking for missed citations

Amending structure

Turning AI into a quiz master/personal tutor/intended audience

Tasks for Employability

Generating interview questions

Identifying key words in job descriptions

Making suggestions for the types of examples to share in cover letters

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We framed our discussion around three uses for AI:

Research

Feedback ("critical friend")

Employability

The sessions could then be built around using AI for those purposes effectively, while also introducing topics like bias, pros and cons for using AI in these ways, e.g. data privacy, copyright, academic integrity, sycophantic responses, etc. All sessions could cover criticality and frameworks that can be applied to this end. Talking about ethics and impact within a 'real-world' scenario will help students connect their use of AI to the broader conversation.

The sessions would cover suitable AI tools for each purpose (and could also potentially link to where they can find that information outside of AI as a caveat).

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Bias in pictures and videos created by GenAI. (e.g., deep fakes, stereotypes, etc.)

How to collaborate with GenAI (instead of just consuming it).

The environmental impact of GenAI.