The role of digital technology in authentic assessment: perspectives of university educators
Bibliographic record
Abstract
Authentic assessment is widely recognised as a strategy for preparing university students for future. Although the use of digital technologies in authentic assessment is increasing, there is limited understanding of why and how educators choose to integrate technology in these practices. We conducted an exploratory qualitative study through semi-structured interviews of 21 university educators from diverse disciplines and institutional contexts to explore their approaches. Using reflexive thematic analysis, we identified four pedagogical purposes guiding technology use: enhancing disciplinary learning, supporting workplace readiness, fostering personal development, and enabling assessment delivery. The findings show that educators often adapt familiar and accessible technologies to support student learning, rather than prioritising novelty or innovation. Technology serves multiple functions across the assessment process, and its effectiveness depends on alignment with pedagogical intent and student needs. This study contributes to a more nuanced understanding of technology integration and offers practical implications for supporting purposeful and student-centred assessment design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".