Diverse perspectives on the digital frontier: Capturing the wide range of experiences and opinions shared by educators as they navigate the benefits and challenges of integrating AI into their classrooms. Image (and typos) generated by Nano Banana.
Source
The Conversation
Summary
Researcher Nadia Delanoy interviewed ten Canadian teachers to explore how generative AI is reshaping K–12 classrooms. The teachers, spanning grades 5–12 across multiple provinces, described mounting pressures to adapt amid ethical uncertainty and emotional strain. Common concerns included the fragility of traditional assessment, inequitable access to AI tools, and rising workloads compounded by inadequate policy support. Many expressed fear that AI could erode the artistry and relational nature of teaching, turning it into a compliance exercise. While acknowledging AI’s potential to enhance workflow, teachers emphasised the need for slower, teacher-led, and ethically grounded implementation that centres humanity and professional judgment.
Key Points
Teachers report anxiety over authenticity and fairness in assessment.
Equity gaps widen as some students have greater AI access than others.
Educators feel policies treat them as implementers, not professionals.
AI integration adds to burnout, threatening teacher autonomy.
Responsible policy must involve teachers, ethics, and slower adoption.
by Brian Mulligan – e-learning consultant with Universal Learning Systems (ulsystems.com)
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:
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.
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.
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.
Rescuing the written word: Exploring innovative teaching and assessment strategies designed to preserve the value and necessity of the traditional essay in the age of generative AI. Image (and typos) generated by Nano Banana.
Source
Inside Higher Ed
Summary
Philosophy instructor Lily Abadal argues that the traditional take-home essay has long been failing as a measure of critical thinking—an issue made undeniable by the rise of generative AI. Instead of abandoning essays altogether, she advocates for “slow-thinking pedagogy”: a semester-long, structured, in-class writing process that replaces rushed, last-minute submissions with deliberate research, annotation, outlining, drafting and revision. Her scaffolded model prioritises depth over content coverage and cultivates intellectual virtues such as patience, humility and resilience. Abadal contends that meaningful writing requires time, struggle and independence—conditions incompatible with AI shortcuts—and calls for designated AI-free spaces where students can practise genuine thinking and writing.
Key Points
Traditional take-home essays often reward superficial synthesis rather than deep reasoning.
AI exposes existing weaknesses by enabling polished but shallow student work.
“Slow-thinking pedagogy” uses structured, in-class writing to rebuild genuine engagement.
Scaffolded steps—research, annotation, thesis development, outlining, drafting—promote real understanding.
Protecting AI-free spaces supports intellectual virtues essential for authentic learning.
Questioning the digital degree: AI-generated work is forcing educators to reassess the integrity and perceived value of completion certificates for online courses. Image (and typos) generated by Nano Banana.
Source
The Conversation
Summary
Mohammed Estaiteyeh argues that generative AI has exposed fundamental weaknesses in asynchronous online learning, where instructors cannot observe students’ thinking or verify authorship. Traditional assessments—discussion boards, reflective posts, essays, and multimedia assignments—are now easily replaced or augmented by AI tools capable of producing personalised, citation-matched work indistinguishable from human output. Detection tools and remote proctoring offer little protection and raise serious equity and ethical issues. Estaiteyeh warns that without systemic redesign, institutions risk issuing credentials that no longer guarantee genuine learning. He advocates integrating oral exams, experiential learning with external verification, and programme-level redesign to maintain authenticity and uphold academic integrity in the AI era.
Key Points
Asynchronous online courses face the highest risk of undetectable AI substitution.
Discussion boards, reflections, essays, and even citations can be convincingly AI-generated.
AI detectors and remote proctoring are unreliable, inequitable, and ethically problematic.
Oral exams and experiential assessments offer partial safeguards but require major redesign.
Institutions must invest in structural change or risk turning asynchronous programmes into “credential mills.”
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.
