Don't believe the hype. AI myths and the need for a critical approach in higher education

Synthesis: An editorial from the Journal of Applied Learning & Teaching that critically interrogates the hype surrounding AI and generative AI (GenAI) in higher education. The authors dismantle eight entrenched myths about AI — including its supposed autonomy, intelligence, objectivity, and inevitability — and argue that these technologies tend to exacerbate inequality, environmental degradation, labor precarity, and academic-integrity erosion. They call on higher-education intellectuals to embed AI Literacy into curricula and institutional practices as the corrective to uncritical technological optimism.

Key Findings

Eight myths, dismantled. The editorial systematically critiques eight prevailing myths that shape the AI discourse: (1) 'AI is artificial'; (2) 'AI is intelligent'; (3) 'AI will make the world a better place'; (4) 'AI is objective and unbiased'; (5) 'the US is the sole AI superpower and Big Tech holds quasi-monopolies'; (6) 'AI will not significantly affect the job market'; (7) 'AI revolutionizes higher education'; and (8) 'teachers can reliably detect AI-generated work'. Each myth is presented with the claims of its proponents and then countered with critical evidence.

AI is neither artificial nor intelligent. Drawing on Crawford (2021), the authors contend AI is not inherently autonomous, intelligent, or objective but a product of human ingenuity built on vast, often exploitative labor — click workers, data annotators, and content moderators — and the wholesale scraping of human-generated, frequently copyrighted content. The term 'artificial intelligence' itself is framed as a marketing construct and 'empty signifier'.

Hype vs. harm to society and the environment. Contrary to claims that AI will engender a more democratic, equal, and sustainable world, the authors argue GenAI threatens democracy (deepfakes, targeted propaganda, automated disinformation), exacerbates inequality and the Digital Divide, and carries a heavy ecological footprint — from rare-mineral extractivism in the Global South to enormous energy and water consumption and offshored e-waste. AI is framed here as a source of AI Misuse and Learning Harm at a societal scale.

Geopolitics and the myth of US dominance. The belief that the US exclusively dominates the AI arena is challenged by China's rapid ascent, illustrated by DeepSeek's Open Source, low-cost reasoning model that disrupted markets in January 2025. This raises questions about access, control, and AI Governance — including the adequacy of US chip-export AI Regulation in Education.

Higher education: erosion, not revolution. The claim that AI revolutionizes higher education overlooks its detrimental effects on Academic Integrity and the erosion of evidence-based pedagogical practices, compounded by an existing crisis of higher education. Likewise, AI Detection is unreliable: models increasingly produce outputs indistinguishable from human work, undermining the belief that teachers can catch AI use with or without AI tools.

The corrective: critical AI literacy. The authors call on intellectuals in higher education to lead a transformative agenda — embedding AI Literacy as a graduate attribute, supported by Educational Development, innovative assessment, and metacognitive initiatives. This is intended to equip learners to critically evaluate digital content, challenge techno-optimistic narratives, and ensure AI serves human insight and social justice rather than perpetuating Critical Thinking-eroding technological illusions.

What this means for practice

• Faculty developers. Embed critical AI Literacy in curricula and course review as a graduate attribute, teaching how AI systems are actually built — on click workers, data annotators, content moderators, and scraped, often copyrighted, human content — rather than presenting them as autonomous and objective.
• Instructors. Teach the eight myths directly, including that AI is neither artificial nor intelligent and that AI detectors are unreliable, so learners can challenge techno-optimistic narratives rather than absorb them.
• Instructors. Treat innovative assessment and metacognitive initiatives as the integrity strategy instead of detection software, since model output is increasingly indistinguishable from human work.
• Administrators. Weigh AI adoption against its equity, labor, and environmental costs — rare-mineral extraction in the Global South, energy and water consumption, offshored e-waste — and resist profit-driven Big Tech narratives in procurement and policy.
• Researchers. Carry the geopolitical critique into AI Governance work: China's ascent (e.g., DeepSeek's low-cost Open Source models) unsettles the single-superpower assumption and raises questions about access, control, and export AI Regulation in Education.

Limitations

• The piece is an editorial in Journal of Applied Learning & Teaching 8(1), not an empirical study or systematic review: it asserts and counters eight myths using secondary literature and recent-events examples, so it cannot establish how common these beliefs are among educators or what effects they have.
• Its evidence is documentary (e.g., Crawford, 2021, on labor and data extraction) and includes fast-moving examples such as DeepSeek's January 2025 release, so claims about market disruption date quickly.
• As a normative argument for embedding critical AI literacy, it evaluates no intervention, no curriculum, and no learners; its recommendations remain untested.

Citation

Rudolph, J., Ismail, F., Tan, S., & Seah, P. (2025). Don't believe the hype. AI myths and the need for a critical approach in higher education. Journal of Applied Learning & Teaching, 8(1).