AI, Analytics and a New Assessment Model for Universities

Abstract

As the COVID-19 pandemic recedes, its legacy has been to disrupt universities across the world, most immediately in developing online adjuncts to face-to-face teaching. Behind these problems lie those of assessment, particularly traditional summative assessment, which has proved more difficult to implement. This paper models the current practice of assessment in higher education as influenced by ten factors, the most important of which are the emerging technologies of artificial intelligence (AI) and learning analytics (LA). Using this model and a SWOT analysis, the paper argues that the pressures of marketisation and demand for nontraditional and vocationally oriented provision put a premium on courses offering a more flexible and student-centred assessment. This could be facilitated through institutional strategies enabling assessment for learning: an approach that employs formative assessment supported by AI and LA, together with collaborative working in realistic contexts, to facilitate students’ development as flexible and sustainable learners. While literature in this area tends to focus on one or two aspects of technology or assessment, this paper aims to be integrative by drawing upon more comprehensive evidence to support its thesis.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 43 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.