Digital Technologies in Authentic Assessment in Higher Education: A Systematic Literature Review and Narrative Synthesis
Bibliographic record
Abstract
Authentic assessment is widely recognized as a valuable method that reflects real-world contexts, allowing students to apply their knowledge to practical challenges and prepare for their futures. Despite the pervasive influence of digital technologies in modern work and life, their role in authentic assessment—an approach centered on real-world relevance—remains poorly understood. Key questions persist regarding how digital technologies are integrated into authentic assessment practices. This review examines the nature and extent of technology integration in authentic assessment within higher education literature. Through a systematic search, identification, analysis, and synthesis, we identified 52 relevant studies. Our findings reveal significant variation in technology use across the four steps of authentic assessment. While digital tools are commonly employed in assessment task design (Step 2), there is limited consideration of broader digital contexts (Step 1) or integration into evaluative judgments and feedback mechanisms (Steps 3 and 4). We also identify diverse approaches to incorporating technology within the design phase. These differences in technology use reflect varying conceptualizations of authentic assessment, influencing its design, implementation, and learning outcomes. To provide educators with practical guidance, we build on a widely adopted stepwise model by introducing a structured framework for integrating digital technologies into authentic assessment. Finally, we highlight areas for future research and practice that may enhance authentic assessment through technology.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.027 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".