As generative artificial intelligence becomes more capable of producing essays and other written work, universities face a difficult question: how can they show that a graduate has genuinely learned to think, communicate and apply knowledge? The debate matters to students in Trinidad and Tobago weighing the cost of higher education against the skills employers need. But evidence that AI is challenging assessment is not the same as evidence that university degrees have lost their value.<\/p>\n
An essay published by Roger Hunt and colleagues argues that generative AI has weakened the evidentiary value of unsupervised university essays. Its central concern is that a polished submission may no longer reliably demonstrate how much disciplinary understanding a student has developed independently. If a student can use an AI system to generate a convincing answer, the finished text alone may offer less information about the student’s own reasoning than it once did.<\/p>\n
The authors propose that universities should require three complementary demonstrations of competence: a defended presentation, a written product appropriate to the discipline, and an applied output that teaches or puts the argument into practice for someone else. They describe the relevant abilities as oracy, literacy and algorithmacy, the latter referring to the ability to use algorithmic systems without surrendering independent judgement to them.<\/p>\n
The essay also cites a randomized trial involving approximately 1,000 Turkish high-school students. According to its account, students given access to GPT performed 48 per cent better on assisted problems but 17 per cent worse on unassisted problems than a control group. The contrast suggests that performance with AI assistance may not necessarily indicate the ability to solve a problem independently. However, the reported results concern a particular study and student group; they cannot, by themselves, establish how AI affects every university subject or every student.<\/p>\n
For higher education, the distinction between using a tool effectively and understanding the underlying material is crucial. AI may help a student organise ideas, explore alternative explanations or work through a difficult concept. But if assessment measures only the quality of the final output, instructors may struggle to determine whether the student can explain, verify or apply that work without assistance. Oral examinations, supervised tasks and practical demonstrations are among the assessment approaches that could help institutions address that problem, although the supplied essay does not establish which combination would work best across all disciplines.<\/p>\n
This debate does not provide a direct calculation of whether a degree is worth its cost in Trinidad and Tobago. The available material contains no comparable local figures for tuition, student debt, graduate earnings, employment rates by qualification or the costs and outcomes of alternative training routes. It therefore cannot establish whether university education produces a better financial return than vocational education, apprenticeships, short courses or entering employment directly.<\/p>\n
A separate source from TECH Global University claims that 99 per cent of its students find jobs after completing their studies. That is an institutional marketing claim, and the supplied excerpt does not provide the underlying methodology, the relevant student population or an independent verification. It should not be treated as a reliable measure of the employment prospects of graduates generally, or as evidence that a particular degree will deliver the same result in Trinidad and Tobago.<\/p>\n
Students deciding whether to enrol need to examine the particular qualification, its total cost, the occupations it can lead to and the evidence available about graduates’ outcomes. They should also consider whether the course teaches skills that remain useful when AI tools are available, including critical judgement, communication, practical application and the ability to check machine-generated work. Employers’ actual hiring requirements and credible programme-level outcome data would help make that comparison more concrete.<\/p>\n
The strongest conclusion supported by the evidence is that AI is putting pressure on universities to demonstrate learning more convincingly. It does not establish that degrees are obsolete or universally worth their price. For students, the relevant question is whether a particular programme develops demonstrable abilities and offers value proportionate to its costs, rather than whether the credential alone guarantees a successful career.<\/p>\n","protected":false},"excerpt":{"rendered":"
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