Month: November 2025

AI Could Revolutionise Higher Education in a Way We Did Not Expect

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by Brian Mulligan – e-learning consultant with Universal Learning Systems (ulsystems.com)
grand, expansive, and ornate university library or academic hall with high ceilings and classical architecture. In the center, a towering, swirling helix of glowing blue digital data, code, books, and educational icons rises dramatically, representing the transformative power of AI. Around the hall, students are seated at tables with glowing laptops, and many more students are walking and interacting. Holographic projections of famous busts and academic figures are subtly integrated into the scene. The entire environment is infused with a futuristic, digital glow. Image (and typos) generated by Nano Banana.
Artificial intelligence is poised to unleash a revolution in higher education, not in the ways we’ve conventionally imagined, but through unexpected and profound transformations. This image visualises AI as a central, dynamic force reshaping academic landscapes, curriculum delivery, and the very nature of learning in universities. Image (and typos) generated by Nano Banana.

The current conversation about Artificial Intelligence (AI) in higher education primarily focuses on efficiency and impact. People talk about how AI can personalise learning, streamline administrative tasks, and help colleges “do more with less.” For decades, every new technology, from online training to MOOCs, promised a similar transformation. Generative AI certainly offers powerful tools to enhance existing processes.

However, perhaps the revolutionary potential of AI in higher education may come from a more critical and urgent pressure: its significant challenge to the integrity of academic credentials and the learning processes they are supposed to represent.

Historically, colleges haven’t had a strong incentive to completely overhaul their teaching models just because new technology arrived. Traditional lectures, established assessment methods, and the value of a physical campus have remained largely entrenched. Technology usually just served to augment existing practices, not to transform the underlying structures of teaching, learning, and accreditation.

AI, however, may be a different kind of catalyst for change.

The Integrity Challenge

AI’s ability to create human-quality text, solve complex problems, and produce creative outputs has presented a serious challenge to academic integrity. Reports show a significant rise in AI-driven cheating, with many students now routinely using these tools to complete their coursework. For a growing number of students, offloading cognitive labour, from summarising readings to generating entire essays, to AI is becoming the new norm.

This widespread and mostly undetectable cheating compromises the entire purpose of assessment: to verify genuine learning and award credible qualifications. Even students committed to authentic learning feel compromised, forced to compete against peers using AI for an unfair advantage.

Crucially, even when AI use is approved, there’s a legitimate concern that it can undermine the learning process itself. If students rely on AI for foundational tasks like summarisation and idea generation, they may bypass the essential cognitive engagement and critical thinking development. This reliance can lead to intellectual laziness, meaning the credentials universities bestow may no longer reliably signify genuine knowledge and skills. This creates an urgent imperative for institutions to act.

The Shift to Authentic Learning

While many believe we can address this just by redesigning assignments, the challenge offers, and may even require, a structural shift towards more radical educational models. These new approaches,which have been emerging to address the challenges of quality, access and cost, may also prove to be the most effective ways of addressing academic integrity challenges.

To illustrate the point, let’s look at three examples of such emerging models:

  1. Flipped Learning: Students engage with core content independently online. Valuable in-person time is then dedicated to active learning like problem-solving, discussions, and collaborative projects. Educators can directly observe the application of knowledge, allowing for a more authentic assessment of understanding.
  2. Project-Based Learning (PBL): Often seen as an integrated flipped model, PBL immerses students in complex, integrated projects over extended periods. The focus is on applying knowledge from multiple modules and independent research to solve real-world problems. These projects demand sustained, supervised engagement, creative synthesis, and complex problem-solving, capabilities that are very hard to simply outsource to AI.
  3. Work-Based Learning (WBL): A significant part of the student’s journey takes place in authentic workplace settings. The emphasis shifts entirely to the demonstrable application of skills and knowledge in genuine professional contexts, a feat AI alone cannot achieve. Assessment moves to evaluating how a student performs and reflects in their role, including how they effectively and ethically integrate AI tools professionally.

AI as the Enabler of Change

Shifting to these models isn’t easy. Can institutions afford the resources to develop rich content, intricate project designs, and robust supervisory frameworks? Creating and assessing numerous, varied, and authentic tasks requires significant time and financial investment.

This is where technology, now including AI itself, becomes the key enabler for the feasibility of these new pedagogical approaches. Learning technologies, intelligently deployed, can help by:

  • Affordably Creating Content: AI tools rapidly develop diverse learning materials, including texts, videos and formative quizzes as well as more sophisticated assessment designs.
  • Providing Automated Learning Support: AI-powered tutors and chatbots offer 24/7 support, guiding students through challenging material, which personalises the learning journey.
  • Monitoring Independent Work: Learning analytics, enhanced by AI, track student engagement and flag struggling individuals. This allows educators to provide timely, targeted human intervention.
  • Easing the Assessment Burden: Technology can streamline the heavy workload associated with more varied assignments. Simple digital tools like structured rubrics and templated feedback systems free up educator time for nuanced, human guidance.

In summary, the most significant impact of AI isn’t the familiar promise of doing things better or faster. By undermining traditional methods of learning verification through the ease of academic dishonesty, AI has created an unavoidable pressure for systemic change. It forces colleges to reconsider what they are assessing and what value their degrees truly represent.

It’s that AI, by challenging the old system so thoroughly, makes the redesign of higher education a critical necessity.

Brian Mulligan

E-learning Consultant
Universal Learning Systems (ulsystems.com)

Brian Mulligan is an e-learning consultant with Universal Learning Systems (ulsystems.com) having retired as Head of Online Learning Innovation at Atlantic Technological University in Sligo in 2022. His current interests include innovative models of higher education and the strategic use of learning technologies in higher education.

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Students using ChatGPT beware: Real learning takes legwork, study finds

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split image illustrating two contrasting study methods. On the left, a student in a blue-lit setting uses a laptop for "SHORT-CUT LEARNING" with "EASY ANSWERS" floating around. On the right, a student in a warm, orange-lit setting is engaged in "REAL LEGWORK LEARNING," writing in a notebook with open books and calculations. A large question mark divides the two scenes. Image (and typos) generated by Nano Banana.
The learning divide: A visual comparison highlights the potential pitfalls of relying on AI for “easy answers” versus the proven benefits of diligent study and engagement, as a new study suggests. Image (and typos) generated by Nano Banana.

Source

The Register

Summary

A new study published in PNAS Nexus finds that people who rely on ChatGPT or similar AI tools for research develop shallower understanding compared with those who gather information manually. Conducted by researchers from the University of Pennsylvania’s Wharton School and New Mexico State University, the study involved over 10,000 participants. Those using AI-generated summaries retained fewer facts, demonstrated less engagement, and produced advice that was shorter, less original, and less trustworthy. The findings reinforce concerns that overreliance on AI can “deskill” learners by replacing active effort with passive consumption. The researchers conclude that AI should support—not replace—critical thinking and independent study.

Key Points

  • Study of 10,000 participants compared AI-assisted and traditional research.
  • AI users showed shallower understanding and less factual recall.
  • AI summaries led to homogenised, less trustworthy responses.
  • Overreliance on AI risks reducing active learning and cognitive engagement.
  • Researchers recommend using AI as a support tool, not a substitute.

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URL

https://www.theregister.com/2025/11/03/chatgpt_real_understanding/

Summary generated by ChatGPT 5

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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
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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