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TRUVACE RECORD VERSION
record: TRV-2026-0319
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-07-20T08:46:27.698905Z
status: published
lens: trace
sector: other
headline: Preparing students for a world shaped by artificial intelligence | Letters
dek: Prof Leo McCann and Prof Simon Sweeney are right to warn that uncritical reliance on artificial intelligence risks bypassing deep learning (Letters, 16 September). But that does not mean large language models have no place in higher education. Used thoughtfully, they can enhance teaching and learning. Graduates will enter a workforce where AI is ubiquitous. To exclude it from education is to send students out unprepared. The task is not to ignore AI, but to teach students how to use it critically. AI can also re…
gain_title: When used thoughtfully in higher education, large language models can enhance teaching and learning by letting students generate and then critique outputs against primary sources.
problem_title: Uncritical reliance on generative AI in university coursework risks bypassing deep learning and degrading students' learning in arts and humanities.
trace_subject: use of large language models / generative AI by university students affecting deep learning and critical skills
gain_reading: When used thoughtfully in higher education, large language models can enhance teaching and learning by letting students generate and then critique outputs against primary sources.
gain_evidence: Used thoughtfully, they can enhance teaching and learning. | they can enhance teaching and learning
problem_reading: Uncritical reliance on generative AI in university coursework risks bypassing deep learning and degrading students' learning in arts and humanities.
problem_evidence: uncritical reliance on artificial intelligence risks bypassing deep learning | generative AI "sabotages and degrades students' learning"
quick_read: On 24 September 2025 The Guardian published three letters responding to warnings about AI in universities. Contributors from Leeds, Hosei, and Nottingham debated whether large language models should be integrated through critical use and redesigned assessment, or whether current practices already allow coursework to be outsourced to AI.

The exchange matters because it frames a near-term workforce where AI is ubiquitous against immediate risks to learning quality and credential value. What remains uncertain from the letters alone is how widespread AI-written coursework actually is, whether process-based evaluation improves learning at scale, and how universities will balance cost pressures with staffing and standards.
limitation: 
tag: Model-prefilled trace
key_points: Authors propose process-based assessment such as learning journals, reflective essays, and oral defences to make reflection unavoidable. | One letter reports arts and humanities attendance around 30% and coursework counting as 100% of grade being written wholly or largely by AI. | Example given of students mischaracterising Henry Ford as a "transformational leader" used to illustrate anachronistic AI output.
rundown: The correspondence responds to a 16 September letter and offers classroom tactics, including asking students to generate an AI response and then critique it against the 1922 text to expose anachronistic terms and lack of historical context.

Writers draw historical parallels to calculators, word processors, and the internet, arguing curricula previously shifted toward reasoning, structure, and information literacy, and note cost implications with a figure of nearly 30,000 for a degree.
sources:
- journalism | The Guardian | https://www.theguardian.com/technology/2025/sep/24/preparing-students-for-a-world-shaped-by-artificial-intelligence | 2025-09-24
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