Principle 3: Develop student’s GenAI literacy and skills

This principle means integrating GenAI within teaching, learning and assessment across a programme of study and within specific modules. There are several necessary steps to achieve Gen AI literacy and skills:

  • Understanding the fundamental structures of GenAI and their practical implications.
  • Increasing student’s critical awareness of the GenAI application you plan to use and the limitations of the technology more generally.
  • Developing necessary skills for students to make the most effective and ethical use of GenAI applications.

Before these can be considered it is important to take a strategic approach to addressing Gen AI Literacy across a programme of study. This is not just an agenda for individual staff – it is an important development for all programme and course teams. Students can rightly expect that GenAI will be used consistently and fairly by all their teaching staff. We can expect legitimate complaints if staff are inconsistent in their approaches – e.g. if different staff apply different restrictions to assessment tasks which make similar demands on student skills and workload. This is conversant with ‘consistency’ and ‘coherence’ elements of the Brookes’ Digitally Enabled Programmes’ guidance

Course teams might like to develop a ‘GenAI map’ which demonstrates where and how GenAI is incorporated into the course/programme. This map could be shared with students as an opportunity for consultation and possible co-creation. As well as informing ongoing practice and dialogue between staff and students, such maps could be invaluable assets during course revalidation. 

Understanding GenAI’s fundamental structures and their practical implications

It is important for students to understand what GenAI is and how it works. 

A growing number of texts and online resources introduce the technology without delving too deeply into the technical complexities. A good place to start is Wolfram’s (2023) ‘What is ChatGPT doing and why does it work?

The following practices will grow students’ understanding of GenAI whilst building disciplinary knowledge and academic competency.

Increase critical awareness of the GenAI application you plan to use and the limitations of the technology more generally

We cannot (and do not) expect every member of staff or student to know the detailed technological underpinnings of GenAI but we do need to be familiar with the fundamental notions of how the technology works. This allows staff and students to have an informed debate on its uses and limitations, including ones we have already identified such as dataset limitations, establishing the validity of the responses (Bendor-Samuel, 2023), and serious concerns about the impact of GenAI upon the environment. These discussions can be used to further disciplinary knowledge and understanding at the same time as digital and AI literacy.

Errors and hallucinations

Critical awareness should include an understanding that the technology generates incorrect or fabricated responses (often described as ‘hallucinations’) and other limitations, e.g. in calculations and in logical reasoning. 

Develop necessary skills for students to make the most effective and ethical use of GenAI applications

The World Economic Foundation has long anticipated the need for a workforce able to effectively use AI. More recently QAA mooted the use of GenAI as a crucial graduate attribute (2023), and UNESCO has made a compelling plea for our graduates to be able to use GenAI ethically, sustainably and for the common good.

Example of a new skill: improving prompts

What is the ‘anatomy’ of a good prompt?

Prompts should include:
1. Request
plus your specific requirements re
2. Context

Request can be:

1. Task (e.g. summarise X or evaluate X or elaborate on X)
2. Question (e.g. what are the limitations of chatbots like ChatGPT?)

Context should cover:
1. Chatbot role/perspective (e.g. Expert? Practitioner?)
2. Audience (e.g. Age?, Location?, Social context?, Experience/expertise?)
3. Social/historical context (e.g. Date?/Timeframe?)
4. Quality level (e.g. Sources? Reference?)
5. Output format (e.g. Tone?, Word length?, Structure and style?)

General issues and concerns about Artificial General Intelligence

There are also debates which students should be familiar with about the long-term future of GenAI and the search for AGI (Artificial General Intelligence) – machines which can act autonomously and learn for themselves. This idea has been popular in science fiction and fantasy for decades (if not centuries), usually associated with robots and often linked to a dystopian science fiction. But organisations such as OpenAI (who produced ChatGPT) are now suggesting that this will become a reality in the fairly near future (as in recent extracts from interviews with Sam Altman, CEO of OpenAI).