Tag: Cognitive Offloading

This is not the end but a beginning: Responding to “Something Wicked This Way Comes”

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By Kerith George-Briant and Jack Hogan, Abertay University Dundee
Estimated reading time: 5 minutes
A conceptual illustration showing a digital roadmap splitting into two distinct, glowing paths, one labeled "Secure Assessment" and the other "Open Assessment." The background blends subtle academic motifs with swirling binary code, symbolizing the strategic integration of Generative AI into higher education assessment practices. Image (and typos) generated by Nano Banana.
Navigating the future: The “Two-Lane Approach” to Generative AI in assessment—balancing secure testing of threshold concepts (Lane 1) with open collaboration for developing AI literacy and critical thinking (Lane 2). Image (and typos) generated by Nano Banana.

O’Mahony’s provocatively titled “Something Wicked This Way Comes” blog outlined feelings we recognised from across the sector, which were that Generative AI (GenAI) tools have created unease, disruption, and uncertainty. In addition, we felt that GenAI provided huge opportunities, and as higher education has led and celebrated innovation in all disciplines over centuries, how this translated into our assessment practices intrigued us. 

At Abertay University, we’ve been exploring the “wicked problem” of whether to change teaching practices through a small-scale research project entitled “Lane Change Ahead: Artificial Intelligence’s Impact on Assessment Practices.” Our findings agree with O’Mahony’s observations that while GenAI does pose a challenge to academic integrity and traditional assessment models, it also offers opportunities for innovation, equity, and deeper learning, but we must respond thoughtfully and acknowledge that there are a variety of views on GenAI.

Academic Sensemaking

To understand colleagues’ perspectives and experiences, we applied Degn’s (2016) concept of academic sensemaking to understand how the colleagues we interviewed felt about GenAI. Findings showed that some assessment designers are decoupling, designing assessments that use GenAI outputs without requiring students to engage with the tools. Others are defiant or defeatist, allowing limited collaboration with GenAI tools but awarding a low percentage of the grade to that output. And some are strategic and optimistic, embracing GenAI as a tool for learning, creativity, and employability.

The responses show the reasons for unease are not just pedagogical; they’re deeply personal. GenAI challenges academic identity. Recognising this emotional response is essential to supporting staff if change is needed.

Detection and the Blurred Line

And change is needed, we would argue. Back in 2023, Perkin et al’s analysis of Turnitin’s AI detection capabilities revealed that while 91% of fully AI-generated submissions were flagged, the average detection within each paper was only 54.8% and only half of those flagged papers would have been referred for academic misconduct. Similar studies since then have continued to show the same types of results. And if detection isn’t possible, setting an absurd line as referred to by Corbin et al is ever more incongruous. There is no reliable way to indicate whether a student has stopped at the point of using AI for brainstorming or has engaged critically with AI paraphrased output. Some may read this and think that it’s game over, however if we embrace these challenges and adapt our approaches, we find solutions that are fit for purpose.

From Fear to Framework: The Two-Lane Approach

So, what is the solution? Our research explored whether the two-lane approach developed by Liu and Bridgeman would work at Abertay, where:

  • Lane 1: Secure Assessments would be conducted under controlled conditions to assure learning of threshold concepts and
  • Lane 2: Open Assessments would allow unrestricted use of GenAI.

Our case studies revealed three distinct modes of GenAI integration:

  • AI Output Only – Students critiqued AI-generated content without using GenAI themselves. This aligned with Lane 1 and a secure assessment method focusing on threshold concepts.
  • Limited Collaboration – Students used GenAI for planning and a minimal piece of output within a larger piece of assessment, which did not allow GenAI use. Students developed some critical thinking, but weren’t able to apply this learning to the whole assessment.
  • Unlimited Collaboration – Students were fully engaged with GenAI, with reflection and justification built into the assessment. Assessment designers reporting that students produced higher quality work and demonstrated enhanced critical thinking.

Each mode reflected a different balance of trust, control, and pedagogical intent. Interestingly, the AI Output pieces were secure and used to build AI literacy while meeting PSRB requirements, which asked for certain competencies and skills to be tested. The limited collaboration had an element of open assessment, but the percentage of the grade awarded to the output was minimal, and an absurd line was created by asking for no AI use in the larger part of the assessment. Finally, the assessments with unlimited collaboration were designed because those colleagues believed that writing without GenAI was not authentic, and they believed that employers would expect AI literacy skills, perhaps not misplaced based on the figure given in O’Mahony’s blog.

Reframing the Narrative: GenAI as Opportunity

We see the need to treat GenAI as a partner in education, one that encourages critical reflection. This will require carefully scaffolded teaching activities to develop the AI literacy of students and avoid cognitive offloading. Thankfully, ways forward have begun to appear, as noted in the work of Gerlick and Jose et al.

Conclusion: From Wicked to ‘Witch’ lane?

As educators, we have a choice. We can resist, decouple from GenAI or we can choose to lead the narrative strategically and optimistically. Although the pathway forward may not be a yellow brick road, we believe it’s worth considering which lane may suit us best. The key is that we don’t do this in isolation, but we take a pragmatic approach across our entire degree programme considering the level of study and the appropriate AI literacy skills.

GenAI acknowledgement:
Microsoft Copilot (https://copilot.microsoft.com) – used to create a draft blog from our research paper.

Kerith George-Briant

Learner Development Manager
Abertay University

Kerith George-Briant manages the Learner Development Service at Abertay. Her key interests are in building best practices in using AI, inclusivity, and accessibility.

Jack Hogan

Lecturer in Academic Practice
Abertay University

Jack Hogan works within the Abertay Learning Enhancement (AbLE) Academy as a Lecturer in Academic Practice. His research interests include student transitions and the first-year experience, microcredentials, skills development and employability. 

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Dr. Strange-Syllabus or: How My Students Learned to Mistrust AI and Trust Themselves

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by Tadhg Blommerde – Assistant Professor, Northumbria University
Estimated reading time: 5 minutes
A stylized image featuring a character resembling Doctor Strange, dressed in his iconic attire, standing in a magical classroom setting. He holds up a glowing scroll labeled "SYLLABUS." In the foreground, two students (one Hispanic, one Black) are seated at a table, working on laptops that display a red 'X' over an AI-like interface, symbolizing mistrust of AI. Above Doctor Strange, a glowing, menacing AI entity with red eyes and outstretched arms hovers, presenting a digital screen, representing the seductive but potentially harmful nature of AI. Magical, glowing runes, symbols, and light effects fill the air around the students and the central figure, illustrating complex learning. Image (and typos) generated by Nano Banana.
In an era dominated by AI, educators are finding innovative ways to guide students. This image, inspired by a “Dr. Strange-Syllabus,” represents a pedagogical approach focused on fostering self-reliance and critical thinking, helping students to navigate the complexities of AI and ultimately trust their own capabilities. Image (and typos) generated by Nano Banana.

There is a scene I have witnessed many times in my classroom over the last couple of years. A question is posed, and before the silence has a chance to settle and spark a thought, a hand shoots up. The student confidently provides an answer, not from their own reasoning, but read directly from a glowing phone or laptop screen. Sometimes the answer is wrong and other times it is plausible but subtly wrong, lacking the specific context of our course materials. Almost always the reasoning behind the answer cannot be satisfactorily explained. This is the modern classroom reality. Students arrive with generative AI already deeply embedded in their personal lives and academic processes, viewing it not as a tool, but as a magic machine, an infallible oracle. Their initial relationship with it is one of unquestioning trust.

The Illusion of the All-Knowing Machine

Attempting to ban this technology would be a futile gesture. Instead, the purpose of my teaching became to deliberately make students more critical and reflective users of it. At the start of my module, their overreliance is palpable. They view AI as an all-knowing friend, a collaborator that can replace the hard work of thinking and writing. In the early weeks, this manifests as a flurry of incorrect answers shouted out in class, the product of poorly constructed prompts fed into (exclusively) ChatGPT, and a complete faith in the response it generated. It was clear there was a dual deficit: a lack of foundational knowledge on the topic, and a complete absence of critical engagement with the AI’s output.

Remedying this begins not with a single ‘aha!’ moment, but through a cumulative, twelve-week process of structured exploration. I introduce a prompt engineering and critical analysis framework that guides students through writing more effective prompts and critically engaging with AI output. We move beyond simple questions and answers. I task them with having AI produce complex academic work, such as literature reviews and research proposals, which they would then systematically interrogate. Their task is to question everything. Does the output actually adhere to the instructions in the prompt? Can every claim and statement be verified with a credible, existing source? Are there hidden biases or a leading tone that misrepresents the topic or their own perspective?

Pulling Back the Curtain on AI

As they began this work, the curtain was pulled back on the ‘magic’ machine. Students quickly discovered the emperor had no clothes. They found AI-generated literature reviews cited non-existent sources or completely misrepresented the findings of real academic papers. They critiqued research proposals that suggested baffling methodologies, like using long-form interviews in a positivist study. This process forced them to rely on their own developing knowledge of module materials to spot the flaws. They also began to critique the writing itself, noting that the prose was often excessively long-winded, failed to make points succinctly, and felt bland. A common refrain was that it simply ‘didn’t sound like them’. They came to realise that AI, being sycophantic by design, could not provide the truly critical feedback necessary for their intellectual or personal growth.

This practical work was paired with broader conversations about the ethics of AI, from its significant environmental impact to the copyrighted material used in its training. Many students began to recognise their own over-dependence, reporting a loss of skills when starting assignments and a profound lack of satisfaction in their work when they felt they had overused this technology. Their use of the technology began to shift. Instead of a replacement for their own intellect, it became a device to enhance it. For many, this new-found scepticism extended beyond the classroom. Some students mentioned they were now more critical of content they encountered on social media, understanding how easily inaccurate or misleading information could be generated and spread. The module was fostering not just AI literacy, but a broader media literacy.

From Blind Trust to Critical Confidence

What this experience has taught me is that student overreliance on AI is often driven by a lack of confidence in their own abilities. By bringing the technology into the open and teaching them to expose its limitations, we do more than just create responsible users. We empower them to believe in their own knowledge and their own voice. They now see AI for what it is: not an oracle, but a tool with serious shortcomings. It has no common sense and cannot replace their thinking. In an educational landscape where AI is not going anywhere, our greatest task is not to fear it, but to use it as a powerful instrument for teaching the very skills it threatens to erode: critical inquiry, intellectual self-reliance, and academic integrity.

Tadhg Blommerde

Assistant Professor
Northumbria University

Tadhg is a lecturer (programme and module leader) and researcher that is proficient in quantitative and qualitative social science techniques and methods. His research to date has been published in Journal of Business Research, The Service Industries Journal, and European Journal of Business and Management Research. Presently, he holds dual roles and is an Assistant Professor (Senior Lecturer) in Entrepreneurship at Northumbria University and an MSc dissertation supervisor at Oxford Brookes University.

His interests include innovation management; the impact of new technologies on learning, teaching, and assessment in higher education; service development and design; business process modelling; statistics and structural equation modelling; and the practical application and dissemination of research.

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New elephants in the GenerativeAI room? Acknowledging the costs of GenAI to develop ‘critical AI literacy’

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by Sue Beckingham, NTF PFHEA – Sheffield Hallam University and Peter Hartley NTF – Edge Hill University
Estimated reading time: 8 minutes
Image created using DALLE-2 2024 – Reused to save cost

The GenAI industry regularly proclaims that the ‘next release’ of the chatbot of your choice will get closer to its ultimate goal – Artificial General Intelligence (AGI) – where AI can complete the widest range of tasks better than the best humans.

Are we providing sufficient help and support to our colleagues and students to understand and confront the implications of this direction of travel?

Or is AGI either an improbable dream or the ultimate threat to humanity?

Along with many (most?) GenAI users, we have seen impressive developments but not yet seen apps demonstrating anything close to AGI. OpenAI released GPT-5 in 2025 and Sam Altman (CEO) enthused: “GPT-5 is the first time that it really feels like talking to an expert in any topic, like a PhD-level expert.” But critical reaction to this new model was very mixed and he had to backtrack, admitting that the launch was “totally screwed up”. Hopefully, this provides a bit of breathing space for Higher Education – an opportunity to review how we encourage staff and students to adopt an appropriately critical and analytic perspective on GenAI – what we would call ‘critical AI literacy’.

Acknowledging the costs of Generative AI

Critical AI literacy involves understanding how to use GenAI responsibly and ethically – knowing when and when not to use it, and the reasons why. One elephant in the room is that GenAI incurs costs, and we need to acknowledge these.

Staff and students should be aware of ongoing debates on GenAI’s environmental impact, especially given increasing pressures to develop GenAI as your ‘always-on/24-7’ personal assistant. Incentives to treat GenAI as a ‘free’ service have increased with OpenAI’s move into education, offering free courses and certification. We also see increasing pressure to integrate GenAI into pre-university education, as illustrated by the recent ‘Back to School’ AI Summit 2025 and accompanying book, which promises a future of ‘creativity unleashed’.

We advocate a multi-factor definition of the ‘costs’ of GenAI so we can debate its capabilities and limitations from the broadest possible perspective. For example, we must evaluate opportunity costs to users. Recent research, including brain scans on individual users, found that over-use of GenAI (or specific patterns of use) can have definite negative impact on users’ cognitive capacities and performance, including metacognitive laziness and cognitive debt. We group costs into four key areas: cost to the individual, to the environment, to knowledge and cost to future jobs.

Cost of Generative AI to the individual, environment, knowledge and future jobs
(Beckingham and Hartley, 2025)

Cost to the individual

Fees: subscription fees for GenAI tools range from free for the basic version through to different levels of paid upgrades (Note: subscription tiers are continually changing). Premium models such as enterprise AI assistants are costly, limiting access to businesses or high-income users.

Accountability: Universities must provide clear guidelines on what can and cannot be shared with these tools, along with the concerns and implications of infringing copyright.

Over-reliance: Outcomes for learning depend on how GenAI apps are used. If students rely on AI-generated content too heavily or exclusively, they can make poor decisions, with a detrimental effect on skills.

Safety and mental health: Increased use of personal assistants providing ‘personal advice’ for socioemotional purposes can lead to increased social isolation

Cost to the environment

Energy consumption – The infrastructure used for training and deploying Large Language Models (LLMs) requires millions of GPU hours to train, and increases substantially for image generation. The growth of data centres also creates concerns for energy supply.

Emissions and carbon footprint – Developing the technology creates emissions through the mining, manufacturing, transport and recycling processes

Water consumption – Water needed for cooling in the data centres equates to millions of gallons per day

e-Waste – This includes toxic materials (e.g. lead, barium, arsenic and chromium) in components within ever-increasing LLM servers. Obsolete servers generate substantial toxic emissions if not recycled properly.

Cost to knowledge

Erosion of expertise – Data is trained on information publicly available on the internet, from formal partnerships with third parties, and information that users or human trainers and researchers provide or generate.

Ethics – Ethical concerns highlight the lived experiences of those employed in data annotation and content moderation of text, images and video to remove toxic content.

Misinformation – Indiscriminate data scraping from blogs, social media, and news sites, coupled with text entered by users of LLMs, can result in ‘regurgitation’of personal data, hallucinations and deepfakes.

Bias – Algorithmic bias and discrimination occurs when LLMs inherit social patterns, perpetuating stereotypes relating to gender, race, disability and protected characteristics

Cost to future jobs

Job displacement – GenAI is “reshaping industries and tasks across all sectors”, driving business transformation. But will these technologies replace rather than augment human work?

Job matching – Increased use of AI in recruitment and by jobseekers creates risks that GenAI is misrepresenting skills. This creates challenges for job-seeker profile analysers to accurately identify skills with candidates that can genuinely evidence them.

New skills – Reskilling and upskilling in AI and big data tops the list of fastest-growing workplace skills. A lack of opportunity to do so can lead to increased unemployment and inequality.

Wage suppression – Workers with skills that enable them to use AI may see their productivity and wages increase, whereas those who do not may see their wages decrease.

The way forward

We can only develop AI literacy by actively involving our student users. Previously we have argued that institutions/faculties should establish ‘collaborate sandpits’ offering opportunities for discussion and ‘co-creation’. Staff and students need space for this so that they can contribute to debates on what we really mean by ‘responsible use of GenAI’ and develop procedures to ensure responsible use. This is one area where collaborations/networks like GenAI N3 can make a significant contribution.

Sadly, we see too many commentaries which downplay, neglect or ignore GenAI’s issues and limitations. For example, the latest release from OpenAI – Sora 2 – offers text to video and has raised some important challenges to copyright regulations. There is also the continuing problem of hallucinations. Despite recent claims of improved accuracy, GenAI is still susceptible. But how do we identify and guard against untruths which are confidently expressed by the chatbot?

We all need to develop a realistic perspective on GenAI’s likely development. The pace of technical change (and some rather secretive corporate habits) makes this very challenging for individuals, so we need proactive and co-ordinated approaches by course/programme teams. The practical implications of this discussion is that we all need to develop a much broader understanding of GenAI than a simple ‘press this button’ approach.  

Reference

Beckingham, S. and Hartley, P., (2025). In search of ‘Responsible’ Generative AI (GenAI). In: Doolan M.A. and Ritchie, L. eds. Transforming teaching excellence: Future proofing education for all. Leading Global Excellence in Pedagogy, Volume 3. UK: IFNTF Publishing. ISBN 978-1-7393772-2-9 (ebook). https://amzn.eu/d/gs6OV8X

Sue Beckingham

Associate Professor Learning and Teaching
Sheffield Hallam University

Sue Beckingham is an Associate Professor in Learning and Teaching, Sheffield Hallam University. Externally she is a Visiting Professor at Arden University and a Visiting Fellow at Edge Hill University. She is also a National Teaching Fellow, Principal Fellow of the Higher Education Academy and Senior Fellow of the Staff and Educational Developers Association. Her research interests include the use of technology to enhance active learning; and has published and presented this work internationally as an invited keynote speaker. Recent book publications Using Generative AI Effectively on Higher Education: Sustainable and Ethical Practices for Learning Teaching and Assessment.

Peter Hartley

Visiting Professor
Edge Hill University

Peter Hartley is now Higher Education Consultant, and Visiting Professor at Edge Hill University, following previous roles as Professor of Education Development at University of Bradford and Professor of Communication at Sheffield Hallam University. National Teaching Fellow since 2000, he has promoted new technology in education, now focusing on applications/implications of Generative AI, co-editing/contributing to the SEDA/Routledge publication Using Generative AI Effectively in Higher Education (2024; paperback edition 2025). He has also produced several guides and textbooks for students (e.g. co-author of Success in Groupwork 2nd Edn ). Ongoing work includes programme assessment strategies; concept mapping and visual thinking.

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Will AI Make You Stupid?

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A digital representation of a human brain with glowing teal data streams and circuit-like patterns flowing out from its right side, against a dark, technical background with a subtle digital frame. Image (and typos) generated by Nano Banana.
Exploring the cognitive impact of artificial intelligence: Will reliance on AI enhance our intellect or diminish our critical thinking abilities? Image (and typos) generated by Nano Banana.

Source

The Economist

Summary

A Massachusetts Institute of Technology study has found that students using ChatGPT during essay-writing tasks showed reduced brain activity in areas linked to creativity and attention. Similar research from Microsoft and the SBS Swiss Business School supports the claim that frequent AI use may diminish critical thinking, fostering “cognitive miserliness,” or the tendency to offload mental effort. While experts caution that the evidence is not yet conclusive, they warn that excessive reliance on AI could erode problem-solving and creative skills over time. Historical parallels—such as Socrates’ scepticism about writing—suggest technological tools often reshape, but do not destroy, cognitive abilities. The article concludes that using AI thoughtfully—prompting step by step and reflecting critically—can help preserve intellectual engagement even as automation advances.

Key Points

  • MIT researchers observed reduced creative and attentional brain activity in AI-assisted students.
  • Frequent AI users performed worse on critical-thinking tests in a Swiss study.
  • Over-reliance on AI can create “cognitive offloading” and feedback loops of dependence.
  • Experts urge reflective, guided use—AI as assistant, not replacement.
  • Strategies such as incremental prompting and “cognitive forcing” can sustain mental effort.
  • Evidence remains mixed: AI may change, but not necessarily weaken, human intelligence.

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URL

https://www.economist.com/science-and-technology/2025/07/16/will-ai-make-you-stupid

Summary generated by ChatGPT 5

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