Supporting Responsible AI Use

(This week the TLC has been exploring how we engage our students in using AI wisely and well.)

As AI tools become part of everyday academic practice, from ChatGPT and Grammarly to NotebookLM and beyond, our students increasingly turn to them for speed, structure and inspiration. We cannot control this. Instead, our shared task is to encourage responsible, reflective and academically grounded use of these tools.

In this week’s bulletin, I want to showcase what we discussed with our students in their recent TLC seminar to explore how we can guide students towards owning their work, engaging with ideas, and developing the judgement that characterises genuine higher learning.

The role of AI and how it can be used well

AI offers fluency, speed and access to a vast archive of prior human output, but it does not think, generate genuinely new insights, or evaluate competing claims. The idea of the ‘stochastic parrot’ needs to ground our thinking when discussing AI’s use in academic assignments. For our students and for their engagement with the content and framework of their work, we need to focus on the notion that AI reproduces patterns rather than producing original reasoning. For students, this means AI should be seen, if at all, as a starting point; a point from which to critique, adapt and contextualise information that is given, rather than something to accept uncritically.

In the session with the students, we saw how AI can sound authoritative even when offering contradictory or superficial responses. The marketing task we reviewed allowed AI to mirror our response and was equally able to frame a campaign as “excellent” or “poor” simply on the direction of the prompt rather than any underlying conceptual reasoning.

For our students, this means AI might work as a generator of plans and ideas that then require their own interpretation, analysis, justification and contextualisation. AI can accelerate routine processes, but unreflective dependence risks undermining the very learning we all value. When students begin by accepting AI’s suggestions uncritically, they risk producing work that is generic, disconnected from course material, and ultimately misaligned with the aims of their programme.

When students depend on AI they ‘outsource’ too much of the thinking process, they lose opportunities to understand the theory and develop their analytical voice. Moreover, as the session discussion emphasised, students who lean heavily on AI risk losing ownership of their responses: they present polished sentences without really engaging with the ideas or participating in the process of developing themselves for the future world of work.  

Helping students to reflect on the limits and appropriate uses of AI is therefore not simply a matter of academic integrity. It is central to building professional judgement, confidence and ethical awareness that will matter far beyond coursework.

Academic programmes aim to develop graduates who interrogate evidence, take positions, justify decisions and adapt ideas to organisational realities. These reflective capacities require students to engage meaningfully with theory and develop intellectual ownership of their work.

How we encourage responsible use

We can encourage responsible use by a). promoting visible learning processes, such as drafts, notes and reading logs; b). showing that coursework is designed to be achievable, through unpacking and providing clear assignment briefs and module materials; c). focusing on ensuring students pose questions that critique AI outputs; d). emphasising student ownership of their work; and e). creating space for open discussion about AI use.

A). Promote ‘AI-visible’ learning processes

• Encourage students to keep drafts, notes, reading logs, maybe on NotebookLM .
• A portfolio approach helps make their thinking traceable and supports assessment of both process and product.
• A portfolio showing how ideas developed over time
  • Multiple drafts saved with dates
  • Evidence of engagement with module readings
  • Use of theory to justify a position
• Transparent acknowledgement of any AI use

B). Show that good coursework is fully achievable without AI

• Our discussion emphasized that tasks are designed to be completed using module materials and core theories.
• Coursework is written in a way that is achievable for all students with detailied instructions and structure
• Students should see AI as optional scaffolding.
• It is essential that they see AI not as a requirement, shortcut, or replacement for reading and concept work.

C). Make reflective questioning routine

• What assumptions has the AI made here?
• Does this fit the theory we studied?
• How would I localise this to a Bahrain, GCC or specific organisational context?
• What alternative interpretation could be argued?
• Framing AI outputs as something to challenge, compare, digest helps students move beyond passive consumption.

D). Emphasise the principle of “owning the work”

• It may be acceptable to use AI as a contributor to the early stages of ideation, but the intellectual contribution from contextualization to evaluation and argument must belong to the student.

E). Create space for students to talk openly about their AI habits

• Normalising honest discussion reduces the temptation to hide AI use and opens opportunities for troubleshooting, developing good habits and shared expectations, as well as considering how this will apply in the real world.

TLC support

The Teaching and Learning Centre can help by supporting assessment design, providing sample activities that build AI literacy, helping develop reflective portfolios, and offering workshops on critical digital skills and reflective practice.

Our responsibility is to guide students towards using AI wisely. By modelling reflective questioning and designing tasks that reward genuine thinking, we build a learning culture rooted in transparency, engagement and academic integrity.