Further into this technological chapter, I’m noticing a few more voices out there who appear to have scaled the cliff of anxiety and inexperience that we’ve all felt (to some degree) when faced with new AI tools.

From what I’ve observed, the roots of a lot of the discourse around AI tool and their discourse is split into 3 categories:

• Snake oil being peddled to hype products and financial investments
• People are buying into the hype in search of the mythical silver bullet (Which, is much less straightforward than it appears — something to write about another day)
• People are genuinely worried about their future and how it’s impacting their industries and practice

I wont address the first two groups (my cynicism can wait for another day). But since the imprecise start of this gold rush, I’ve been thinking a lot about the third group — which I hypothesise is the largest share of the AI-concious population.

Different industries have had vastly different experiences to this age, a complete picture is outside my scope of expertise. Creative industries for instance have had to face quasi-existential questions in response to the advent of generative AI, which I’m not equipped to answer. My industry of software engineering has probably been the most receptive industry as there are less issues with ownership of code. Aside from ill-advised layoffs, I’ve relatively made peace with it here. But in academia and education, I’ve been considerably more worried.

I want to focus on the realm of education today, because I think there’s some emergent lessons here (pun intended) that may have wider application to our collective experience today.

Education is more than a passion for me. I think it’s a source of hope, and core to the human experience in how we tell stories and help one another. It’s why I love mentorship opportunities throughout my career. It seems my roots as a mathematics tutor have never left me, and don’t plan on going anytime soon

A year ago, an article by Piers Gully — an English professor at UVA — shared his experience in the modern classroom with the “homework machine”. As I had feared, his personal account revealed how AI was negatively affecting students critical reasoning skills. When you’re being judged on your performance, it’s hard to argue against using these tools since your peers are likely to be using them too, leading to an imbalance of grades where higher grades are no longer proportional to the amount of work you put into your submissions.

As a consequence, the overuse of this crutch is seriously impacting the refinement of critical thinking skills. The emphasised quote touches on — what I think — is the only productive way out of this mess:

In the present technological moment, this may be the only choice we have, students and teachers alike: whether or not to fall back on trust.

This morning an article by Tomasz Głowacki showed me a concrete idea on how we can use trust to address the AI issues in the classroom. He writes about a student who felt they were in a genuine impasse with submitting an assignment. They observe that AI is becoming standard in the workforce, so they need practice to stay ahead of the curve. But how can they effectively do this on top of learning the fundamental critical thinking skills?

In a nutshell: treat your students as equals, talk to them about it. Provide a moderated forum for them to discuss and negotiate an contract for AI use:

At my next lecture, I projected a blank screen onto the wall and invited my students to negotiate an “AI contract.” At first, they were guarded, but as I shared my own experience with AI, the classroom dynamic shifted. We stopped playing cat and mouse and became partners. Students opened up about their AI use and began to ask questions. After some debate, we drew a line separating mechanical churning from actual thinking. Automating repetitive tasks or literature searches was acceptable. Bypassing critical analysis was not: System architecture and design would remain strictly human tasks.

AI tools are powerful, and like it or not they’re here to stay. But the scope for using them incorrectly is far wider than any tool that has come before. Establishing an AI contract puts up guardrails, and helps share learning that help others use AI to genuinely benefit without causing more problems than they set out to solve (like sacrificing ones own development and learning).

It’s not perfect, after all there will be those who decide not to follow the rules since course grades have a string influence on future career opportunities. But this could paired with more fair assessment methods where AI cannot help. I’m a supporter of weighting oral presentation assessments, but I worry this introduces bias against those who don’t speak English as a first language or others in our neurodiverse community. A recent method I’ve heard sounded quite interesting though:

• Assess students on the (handwritten) notes they take throughout classes leading up to an assessment

I’m sure there are more strategies to come. Taking a step back, I feel this advice might help with establishing boundaries with people you work with by clarifying what we agree is acceptable (and what is unacceptable)

In my field of software engineering, I would argue in favor of the following clauses in my “AI contract”:

• We want to minimise work — not expand it. No slop grenades!

• You are responsible for what you commit, you must understand your own proposal even if AI writes it

• Integrity matters. AI authored sections should be declared as such

I can see some potential in this strategy broadly, and I hope it helps address some of the anxieties we have with AI in our daily routines. With clear communication, shared goals and understanding, I feel there is hope for our students and the next generations of society to get the most out of their education while also learning how to improve their practice further with AI.