AI is destroying my CS degree
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A UK computer science student describes frustration with their university's inconsistent AI policies, which have alternated between banning and mandating LLM use. They report that AI-generated coursework briefs contain errors and inflated workloads, that tutors are overloaded and rely on automated dashboards, and that constant headlines about software engineering being obsolete are discouraging. The post is a first-person personal account.
One primary source; other links are context, not independent corroboration.
- Primary sourceHacker News · AI
- Discovery linkHacker News
- Linked in sourcehttps://openprocessing.org/@u304330/1557487
- Linked in sourcehttps://openprocessing.org/@u304330/2205896
- Linked in sourcehttps://openprocessing.org/@u304330/1651274
A community post points to the original link. Discussion and upvotes do not establish accuracy.
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- Fetched source page+30
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- Community link+0
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View full scoring →Read the scoring rubric →What the source says
A UK computer science student describes frustration with their university's inconsistent AI policies, which have alternated between banning and mandating LLM use. They report that AI-generated coursework briefs contain errors and inflated workloads, that tutors are overloaded and rely on automated dashboards, and that constant headlines about software engineering being obsolete are discouraging. The post is a first-person personal account.
A READER'S EVIDENCE CHECKHow to read this signalSix questions about claims, sources, risks, and open checks
This is a signal-level guide until claim-level evidence is available. “Not recorded” means the evidence has not been assessed here; it does not mean a result is false. How we check AI news →
- 01Who is making each claim?
The claimant and whether each claim was measured or asserted are not recorded in this public brief.
- 02What was actually tested?
Not recorded per claim. Check the source for sample size, tasks, model and benchmark versions, and reproducibility.
- 03Where are the sources?
Original link: lordtoots.bearblog.dev. Brief basis: fetched source page. Primary page date and quote locations are not established in this panel.
- 04What could mislead?
Source claims not independently checked; this is one student's personal account of their own university, not a verified institutional record.
- 05What is at risk if wrong?
Not assessed per claim. Consider safety, privacy, cost, and adoption decisions before relying on the result.
- 06What would change the view?
No claim-specific check is recorded. An inspectable primary method or independent replication could change the assessment.
Key findings from the stored brief
- The university reversed AI policies multiple times, mandating AI use in some modules and penalizing LLM use in later ones.
- Students report assignment briefs apparently generated with AI, with errors often corrected only a week or two before deadlines.
- The author says assignment workload has roughly tripled, with a 3,000-word assignment containing 15 deliverables.
- Tutors are reportedly overloaded, with contact driven by a data dashboard flagging students as 'at risk'.
Source claims not independently checked; this is one student's personal account of their own university, not a verified institutional record.
This is a source-based abstract, not independent validation of the source's claims.
Evidence & sources
These links show where the signal came from and what the source page linked to. A linked page is context, not independent corroboration.
A community post points to the original link. Discussion and upvotes do not establish accuracy.
Timeline & updates
- Signal dated by source or discovery feed
This timestamp places the signal in the radar. It may be the discovery post date rather than the primary page's first publication date.
- Shared brief stored
AINewsIQ prepared one abstract from fetched source page.
A publication revision history is not stored for this signal. New engagement snapshots do not mean the source itself changed.
Why this may matter
The account illustrates how rapid, inconsistent institutional adoption of AI tools can affect coursework quality, staff workload, and student learning, relevant to education policy decisions.
Limits and open questions
Source claims not independently checked; this is one student's personal account of their own university, not a verified institutional record.
Organization-specific financial, security, and operational effects require the organization's own context and review.
Discussion
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