Full recording: Listen / watch here

This post is a AI/ me hybrid summary of the transcript of a conversation I had with Prof Oz Acar as part of the AI conversations series at KCL.  This morning I found that my Copilot window now allows me to upload attachments (now disabled again! 30/4/24) but the output with the same prompt was poor by comparison to Claude or my ‘writemystyle’ custom GOT unfortunately (for now and at first attempt). I have made some edits to the post for clarity and to remove some of the wilder excesses of  ‘AI cringe’.  

 

“The beauty of PAIR is its flexibility,” Oz explained. “Educators can customise each component based on learning objectives, student cohorts, and assignments.” An instructor could opt for closed problem statements tailored to specific lessons, or challenge students to formulate their own open-ended inquiries. Guidelines may restrict AI tool choices, or enable students more autonomy to explore the ever-expanding AI ecosystem.  That oversight and guidance needs to come from an informed position of course.

 

Crucially, by emphasising skills like problem formulation, iterative experimentation, critical evaluation, and self-reflection, PAIR aligns with long-established pedagogical models proven to deepen understanding, such as inquiry-based and active learning. “PAIR is really skill-centric, not tool-centric,” Oz clarified. “It develops capabilities that will be invaluable for working with any AI system, now or in the future.”

 

The early results from over a dozen King’s modules across disciplines like business, marketing, and arts have piloted PAIR have been overwhelmingly positive. Students have reported marked improvements in their AI literacy – confidence in understanding these technologies’ current capabilities, limitations, and ethical implications. “Over 90% felt their skills in areas like evaluating outputs, recognising bias, and grasping AI’s broader impact had significantly increased,” Oz shared.

 

While valid concerns around academic integrity have catalysed polarising debates, with some advocating outright bans and restrictive detection measures, Oz makes a nuanced case for an open approach centred on responsible AI adoption. “If we prohibit generative AI for assignments, the stellar students will follow the rules while others will use it covertly,” he argued. “Since even expert linguists struggle to detect AI-written text reliably (especially when it has been manipulated rather than simply churned from a single shot prompt), those circumventing the rules gain an unfair advantage.”

 

Instead, Oz advocates assuming AI usage as an integrated part of the learning process, creating an equitable playing field primed for recalibrating expectations and assessment criteria. “There’s less motivation to cheat if we allow appropriate AI involvement,” he explained. “We can redefine what constitutes an exceptional essay or report in an AI-augmented age.”

 

This stance aligns with PAIR’s human-centric philosophy of ensuring students remain firmly in the driver’s seat, leveraging AI as an enabling co-pilot to materialise and enrich their own ideas and outputs. “Throughout the PAIR process, we have mechanisms like reflective reports that reinforce students’ ownership and agency … The AI’s role is as an assistive partner, not an autonomous solution.”

 

Looking ahead, Oz is energised by generative AI’s potential to tackle substantial challenges plaguing education systems globally – from expanding equitable access to quality learning resources, to easing overstretched educators’ burnout through intelligent process optimisation and tailored student support. “We could make education infinitely better by leveraging these technologies thoughtfully…Imagine having the world’s most patient, accessible digital teaching assistants to achieve our pedagogical goals.”

 

However, Oz also acknowledges legitimate worries about the perils of inaction or institutional inertia. “My biggest concern is that we keep talking endlessly about what could go wrong, paralysed by committee after committee, while failing to prepare the next generation for their AI-infused reality,” he cautioned. Without proactive engagement, Oz fears a bifurcated future where students are either obliviously clueless about AI’s disruptive scope, or conversely, become overly dependent on it without cultivating essential critical thinking abilities.

 

Another risk for Oz is generative AI’s potential to propel misinformation and personalised manipulation campaigns to unprecedented scales. “We’re heading into major election cycles soon, and I’m deeply worried about deepfakes fuelling conspiracy theories and political interference,” he revealed. “But even more insidious is AI’s ability to produce highly persuasive, psychologically targeted disinformation tailored to each individual’s profile and vulnerabilities.”

 

Despite these significant hazards, Oz remains optimistic that responsible frameworks like PAIR can steer education towards effectively harnessing generative AI’s positive transformations while mitigating risks.

 

PAIR Framework- Further information

Previous conversation with Dan Hunter

Previous conversation with Mandeep Gill Sagoo

Generative AI in HE- self study short course

An additional point to note: The recording is of course a conversation between two humans (Oz and Martin) and is unscripted. The Q&A towards the end of the recording was faciliated by a third human (Sanjana). I then compared four AI transcription tools: Kaltura, Clipchamp, Stream and Youtube. Kaltura estimated 78% accuracy, Clipchamp crashed twice, Stream was (in my estimation) around 90-95% accurate but the editing/ download process is less convenient when compared to YouTube in my view so the final transcript is the one initially auto-generated in in YouTube, ChatGPT punctuated then re-edited for accuracy in YouTube. Whilst accuracy has improved noticeably in the last few years the faff is still there. The video itself is hosted in Kaltura.