Artificial intelligence is no longer a speculative future — it is reshaping how students research, how educators teach, and how entire information ecosystems supply the raw material of thought. Mount Royal University’s recent feature frames this moment as both opportunity and warning: large language models (LLMs) such as ChatGPT, Microsoft Copilot and Google Gemini are powerful tutors and time-savers — but they also risk hollowing out the generative habits that produce durable learning and original ideas.
LLMs are a class of AI that model human language by training on very large text corpora; they can summarize, explain, translate and generate text with fluency that often passes for human reasoning. Modern LLMs are continuously improved through retraining, fine-tuning, and human-in-the-loop feedback, and are now embedded into mainstream consumer and enterprise products—from ChatGPT to Microsoft’s Copilot family and Google’s Gemini. These tools are now core components of search, productivity software and classroom aids. Mount Royal’s reporting highlights three intersecting, urgent themes:
This is not only a pedagogical problem but a systems-level risk: models trained on a web increasingly filled with derivative or machine-made content risk learning to imitate amplified mediocrity, producing outputs that echo each other rather than surfacing fresh thinking. The danger is circular: AI trained on homogenized internet content reproduces a narrower set of expressions, making the web yet more homogenized — an effect sometimes called model collapse or “training on slop.”
Source: Mount Royal University An intelligent choice: Learning to think in the age of LLMs | MRU
Background / Overview
LLMs are a class of AI that model human language by training on very large text corpora; they can summarize, explain, translate and generate text with fluency that often passes for human reasoning. Modern LLMs are continuously improved through retraining, fine-tuning, and human-in-the-loop feedback, and are now embedded into mainstream consumer and enterprise products—from ChatGPT to Microsoft’s Copilot family and Google’s Gemini. These tools are now core components of search, productivity software and classroom aids. Mount Royal’s reporting highlights three intersecting, urgent themes:- The pedagogical tension between fast answers and learning through struggle.
- The narrowing of the public information ecosystem as automated content and algorithmic curation concentrate visibility.
- Practical classroom and policy experiments that attempt to preserve learning outcomes while allowing students to harness LLM assistance.
Why this matters: the technology and the ecology
What LLMs are and how they evolve
Large Language Models learn statistical patterns from massive text datasets: books, websites, forums and code. Training is computationally intensive and typically followed by targeted techniques such as instruction tuning and reinforcement learning from human feedback (RLHF) to make outputs more useful and aligned with human intent. The result is a broadly capable predictive engine that can generate long-form prose, explain technical concepts, and adapt style on demand. These are not static products: vendors retrain, fine-tune, and deploy improvements continuously, and many production systems also incorporate user feedback into iterative updates. This continuous-improvement model explains both the rapid rise in capability and the ongoing risk profile: models get better at producing persuasive text — including plausible errors — and the web they learn from is itself changing under the pressure of automated content production.The search and attention economy: why first-page results matter
Search remains the primary gateway to knowledge for most users. In countries such as Canada, Google commands overwhelming market share — commonly reported near or above 85–90% of search traffic — which concentrates attention and traffic patterns. That single-platform dominance amplifies the consequences when content on page one is narrow, sponsored, or algorithmically amplified. User behavior further amplifies the effect. Modern analyses show that clicks concentrate on the top results: depending on SERP layout, the first organic position receives a large share of clicks (studies report figures roughly from the high-20s up to mid-30s percent for many SERP types), and click-through drops steeply thereafter. In short, ranking matters enormously — and algorithms that favor monetized or heavily redistributed content can narrow the set of visible ideas.The echo chamber and the “dead internet” problem
Monetization, homogenization, and feedback loops
Mount Royal’s feature warns that monetization-driven exposure (sponsored results, promoted content) and aggregated republishing have eroded variety online. When popular material is republished, aggregated and then re-indexed, automated pipelines re-amplify it; search and ranking systems then tend to surface the same small set of profitable items repeatedly. The result is an information ecosystem that favors what is already popular or monetizable over what is novel or demanding of effort.This is not only a pedagogical problem but a systems-level risk: models trained on a web increasingly filled with derivative or machine-made content risk learning to imitate amplified mediocrity, producing outputs that echo each other rather than surfacing fresh thinking. The danger is circular: AI trained on homogenized internet content reproduces a narrower set of expressions, making the web yet more homogenized — an effect sometimes called model collapse or “training on slop.”
How much of the web is synthetic or bot-driven?
Estimates vary, but multiple industry studies in recent years have documented a significant rise in automated traffic and AI-generated content. Security and analytics reports indicate that automated (non-human) traffic has moved toward parity with human traffic in measured web-requests, and SEO research suggests that a large fraction of newly published articles can be algorithmically or partially machine-generated. These figures are estimates with methodological caveats — detectors are imperfect, datasets (like Common Crawl) omit paywalled human content, and “automation” includes benign crawlers — but the trend is clear: synthetic output and automated traffic are now major forces in the online information supply chain. Treat the exact percentages as provisional; the direction and scale of change are what matter.Learning, cognition and the “amputation” metaphor
Cognitive offloading and McLuhan’s auto-amputation
Historical analogies are instructive. The introduction of the calculator shifted expectations about arithmetic fluency; storing phone numbers in a device changed what memory is expected. Marshall McLuhan’s auto-amputation idea warns that technologies often remove the need to exercise some capability. LLMs can externalize complex generative work — argument scaffolding, drafting, synthesis — and if those practices are no longer regularly exercised, the long-term fluency that comes from repeated practice may atrophy. Mount Royal’s faculty voices underscore this: learning through struggle and iterative failure is a key engine of innovation and critical thinking.Empirical evidence from classrooms
A randomized classroom experiment, reported by Cambridge University Press & Assessment in collaboration with Microsoft Research, offers practical insight: students who relied solely on an LLM for comprehension scored worse on delayed retention tests than students who took notes (either unaided or combined with LLM assistance). The hybrid approach — LLM plus intentional note-taking — matched note-taking alone, suggesting LLMs can scaffold comprehension but do not replace the memory benefits of generative effort unless students still do the generative work themselves. This is the operational finding that should guide pedagogy: design activities so that LLMs augment rather than replace generative learning.Classroom practice: policy, pedagogy and governance
Examples of campus responses
Some instructors are already building robust, transparent policies. A business-course policy cited in Mount Royal requires students to disclose AI use, provide process evidence (showing prompts and intermediate versions), fact-check outputs, and keep reflective, human-authored work separate. Instructors have translated this into concrete assessment rules: if the final answer is wrong, but the student shows valid process and reasoning, partial credit may be awarded; blind copy‑paste from an LLM yields academic penalties. These approaches preserve learning while acknowledging generative AI’s legitimate role as an assistant.Practical classroom design patterns
- Require an unaided first draft or attempt before AI assistance.
- Insist on process logs: prompts, model outputs, and student revisions.
- Grade for process as well as product: reward evidence of thinking, paraphrase, and synthesis.
- Teach prompt literacy and verification skills explicitly: ask students to request sources, check claims and correct factual errors.
- Use in‑tool nudges and artifacts: forced paraphrase, reflection prompts before export, and provenance displays in classroom-facing LLMs.
Systemic risks beyond the classroom
Hallucinations, bias and privacy
LLMs can produce confidently wrong statements (hallucinations), replicate and amplify biases embedded in training data, and — when used with public, unmanaged APIs — leak sensitive content. In enterprise or learner contexts, unmanaged LLM use can create compliance, privacy and IP risk unless governance controls — managed enterprise LLMs, data residency, audit logs — are in place. Mount Royal’s discussions urge institutions to adopt enterprise-grade deployments and policies that protect minors and sensitive data.Homogenized style and cultural loss
Beyond accuracy, there is a subtler cultural risk: if machine-preferred phrasing and templates seep into mass communication, stylistic diversity and rhetorical originality can decline. Large-scale corpus work and observational studies show measurable upticks in certain phrasing patterns after widespread LLM exposure. Organizations that value brand voice or distinctive technical writing must actively manage style governance to resist “AI-speak” drift.Verifying key technical claims (what the evidence says)
- Google’s dominant share of search traffic in Canada tallies near 85–90% in independent analytics (StatCounter and SimilarWeb metrics show Google near ~89% in recent snapshots), validating the claim that search concentration is large and consequential.
- Click distribution studies indicate the first organic search result captures a major slice of clicks (SISTRIX reports ~28.5% average CTR for the first organic result across large keyword samples; behaviour varies by SERP layout). The widely quoted notion that “60% click the first entry” is inconsistent with these large-scale analytics and should be treated as either contextual (a specific SERP layout) or anecdotal rather than universal. Where precise click‑share numbers matter, rely on recent CTR analyses that account for SERP features.
- Automated traffic and AI-generated content are significant and growing. Security industry reporting (Imperva’s Bad Bot reporting and subsequent analyses) finds non-human traffic approaching parity with human traffic in measured web requests; SEO sampling studies (e.g., Graphite’s analysis of Common Crawl samples) estimate that a substantial fraction of newly published articles show signals of machine generation. These studies have methodological limits (detection accuracy, sampling bias, exclusion of paywalled human content), so numerical claims should be framed as evidence of scale and trend rather than exact quantities.
- LLMs are trained on massive corpora and refined through fine-tuning and RLHF; standard descriptions from machine-learning authorities and practitioner guides confirm the core mechanics (pretraining on trillions of tokens, tokenization, self-supervised objectives, transformer architecture). These technical basics underpin why LLMs generalize and why their outputs can be simultaneously fluent and fragile.
Recommendations for educators, IT leaders and Windows users
- Prioritize hybrid workflows: require an unaided attempt followed by an AI-assisted revision stage. This preserves the practice necessary for skill acquisition and lets LLMs accelerate iterative improvements.
- Enforce process evidence: mandate that any AI usage be accompanied by prompt logs, intermediate drafts, and a short reflective justification of why the student accepted or edited the model’s output.
- Adopt managed LLM deployments: prefer education/enterprise plans with no-training clauses on student data, audit logs and data residency controls to minimize privacy and IP risk.
- Teach verification as core digital literacy: include source-checking, triangulation, and confidence-skepticism exercises in curricula.
- Preserve AI-free practice windows: schedule assignments or in-class sessions without AI tools so students exercise unaided problem solving and generative writing.
- Monitor stylistic drift: run periodic corpus checks for “AI-speak” and update style guides to protect brand voice and disciplinary conventions.
- Design technical safeguards at the UI level: implement mandatory paraphrase steps, provenance markers, and export gating when integrating Copilot-like tools into note-taking apps.
Strengths of the current moment — and the realistic path forward
There are clear, practical benefits to LLMs: improved accessibility for students who need scaffolding, dramatic productivity gains for professionals, and new modes of creative exploration. Mount Royal’s coverage and the classroom experiment both demonstrate that when used intentionally and scaffolded by pedagogy, LLMs amplify comprehension without destroying retention. The right strategy is not panic but stewardship: adopt tools while redesigning assessment, instruction and governance to protect the generative skills that matter.Caveats and unverifiable claims
Several widely circulated numeric claims in public debate are estimates with important caveats. Examples:- Any single, global percentage claiming that “50% of internet content is AI‑generated” depends heavily on sampling choices, detector accuracy, and the inclusion/exclusion of paywalled human content. These numbers are indicators of scale, not definitive counts. Treat such figures as provisional.
- Click-distribution claims must be read against SERP heterogeneity: CTR for position #1 varies substantially with featured snippets, sitelinks, shopping boxes, and AI Overviews. A single percentage applied universally is misleading; the right approach is SERP-aware analytics.
Conclusion
The arrival of LLMs marks a pivotal moment for education, work and public information. The choice confronting institutions and individuals is not whether LLMs will persist — they will — but how to integrate them without surrendering the cognitive practices that produce originality, expertise and durable learning. The evidence favors a middle path: teach students to use LLMs as tutors and editors, not as substitutes for thought; redesign assessments to reward process as well as product; and adopt governance that preserves privacy and curricular integrity. That approach turns LLMs into tools for thought instead of crutches — and preserves the messy, essential labor of learning that makes innovation possible.Source: Mount Royal University An intelligent choice: Learning to think in the age of LLMs | MRU
