• 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).

• 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

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.

Skills benchmark (can I do this myself?)

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

• 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

• 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

Where will they be?

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What kind of students might we expect?