RETHINKING ASSESSMENT IN SCIENCE EDUCATION IN THE AGE OF GENERATIVE AI
Keywords:
Generative AI, science education, assessment validity, academic integrity, AI literacy, Nigerian educationAbstract
Generative AI has disrupted educational assessment by threatening the inferential basis on which validity depends. When submitted work may represent either authentic reasoning or algorithmic output, assessment cannot support valid inferences about competence. Detection and prohibition approaches fail technically, conceptually, and behaviourally: students are driven by performance expectations, not policy. This paper proposes a four-principle framework for restoring assessment validity in AI-rich environments, grounded in validity theory (Kane, 2006, 2013; Messick, 1989) and contextualised within Nigerian science education. The framework integrates established traditions of authentic assessment, epistemic documentation, and equitable design, recontextualised under generative AI conditions in a resource-constrained setting where existing frameworks assume technological access that most Nigerian schools do not possess. The framework makes reasoning visible and evaluable through task design and proportional documentation, removing substitution incentives rather than attempting identification. Its validation rests on theoretical coherence and contextual appropriateness, pending empirical testing. Critically, the framework measures reasoning articulation (students' capacity to explain their thinking) as a proxy for reasoning execution (actual cognitive work), a shift that requires explicit acknowledgement. For Nigerian science education, it provides concrete operationalisation of assessment principles aligned with curriculum policy while directly addressing the AI validity crisis.
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