Published (open access): Tsiligkiris, V. (2026). From authentic products to authenticated processes: A systematic conceptual review of authentic assessment in AI-rich higher education. Assessment & Evaluation in Higher Education. https://doi.org/10.1080/02602938.2026.2695376
This whitepaper summarises the peer-reviewed article behind the framework used by this application. The article is published under a Creative Commons Attribution (CC BY 4.0) licence.
Executive Summary
Generative AI has sharpened a long-standing problem: most assessment still relies on polished final products, yet those products can now be generated or substantially improved by AI tools (Dawson et al., 2024; Kofinas et al., 2025). The question is no longer simply whether students used AI. It is whether an assessment still provides valid evidence of what students know, can do, and can justify (Kane, 2013; Messick, 1994).
The framework presented here treats authenticity as a design orientation rather than a task type. It synthesises 37 sources from a systematic conceptual review into six interdependent dimensions (Tsiligkiris, 2026). Each dimension strengthens a different link between what students submit and what can validly be inferred about their capability.
Assessment
The central shift is from authentic products to authenticated processes: a credible-looking report, portfolio, or policy brief is not enough on its own. Valid assessment in AI-rich conditions also needs visible reasoning, verification, judgement, and responsibility (Bearman et al., 2024; Tsiligkiris, 2026).
Why Reframing Matters
Traditional product-oriented tasks often separate learning from the contexts in which graduates will act, which limits the evidence they provide about judgement in realistic conditions (Ashford-Rowe et al., 2014; Wiggins, 1998). Authentic assessment responds by asking students to apply knowledge in meaningful professional, civic, disciplinary, or research contexts (Gulikers et al., 2004; Villarroel et al., 2018).
Authenticity, however, is not workplace imitation. It can involve meaningful engagement, civic contribution, disciplinary identity, and social purpose, and it looks different in nursing, law, engineering, the humanities, and business (Ajjawi et al., 2024; McArthur, 2023). What matters is whether the task creates a credible context for the intended learning.
Generative AI adds an evidential problem the article calls construct substitution: an assessment may appear to measure student learning while actually measuring the performance of a tool. Fully AI-generated submissions have passed live examination systems largely undetected, and experienced markers do not reliably identify AI-mediated work (Kofinas et al., 2025; Scarfe et al., 2024). Product resemblance is therefore an increasingly weak signal of capability (Dawson et al., 2024).
The Six-Dimension Framework
Each dimension asks one design question of a brief and generates a distinct kind of evidence.
1. Contextual fidelity and consequential relevance. Does the task place students in a credible context with a clear purpose and audience? Realism should carry consequence, through a stakeholder, brief, or public output, not mere resemblance (Ashford-Rowe et al., 2014; Gulikers et al., 2004).
2. Cognitive demand and evaluative judgement. Does the task require interpretation, trade-offs, and defensible judgement rather than reproduction? Evaluative judgement, the capacity to appraise the quality of work, becomes central when machines can draft plausibly (Bearman et al., 2024; Villarroel et al., 2018).
3. Process transparency and assessment integrity. Does the design make development visible, through staged submissions, decision rationales, feedback use, or oral defence, so the final product can be interpreted as evidence of learning (Boud, 2000; Kane, 2013)?
4. Student agency and bounded choice. Can students shape topic, case, medium, or data within a common architecture of outcomes and criteria, so that choice adds meaning without undermining comparability (Ajjawi et al., 2024)?
5. Inclusivity and representational fairness. Are expectations, resources, and routes to achievement equitable? Realistic tasks can privilege prior access to professional cultures; transparent criteria and scaffolding keep the assessment focused on the intended outcomes (Tai et al., 2023).
6. AI-aware validity and ethical practice. Is the role of AI explicit and accountable? Permitted use is specified; students disclose, verify, critique, and take responsibility for AI-supported work (Corbin et al., 2026; Perkins et al., 2024).
From Products to Processes
The dimensions are interdependent, and their interdependence is best read through validity theory: fidelity without cognitive demand yields superficial simulation; agency without inclusion reproduces uneven opportunity; process evidence without alignment creates workload without evidential value (Kane, 2013; Messick, 1994). AI-aware validity acts both as a dimension and as a cross-cutting condition that sharpens the other five.
The framework does not classify assessments as authentic or inauthentic. It surfaces specific strengths, validity risks, and redesign priorities, and it treats competent, disclosed AI use as a legitimate object of assessment where that matches the intended construct (Corbin et al., 2026; Fawns et al., 2025).
Using the Framework
Before an assessment is released, a module or programme team reviews the brief against each dimension: what evidence does it generate, and what risk remains unaddressed? Not every task needs to maximise all six. Across a programme, some assessments emphasise fidelity and public communication while others foreground process, dialogue, or judgement. What matters is that choices are deliberate and defensible (Tsiligkiris, 2026).
The published article includes an indicative four-point alignment scale, from weak to strong alignment, to make the basis of design judgements explicit and comparable. It is a heuristic for team deliberation, not a psychometric instrument.
How This Application Applies the Framework
This application operationalises the framework directly. The evaluation engine scores an assessment brief against the six dimensions, returning evidence, risks, and prioritised recommendations. The Assessment Studio generates briefs aligned with the framework and evaluates them in the same workflow, so design and review form one loop. Criteria, scoring rules, and thresholds are configurable, allowing institutions to adapt the framework to their own regulations and disciplinary contexts.
Conclusion
Authentic assessment is a multidimensional design orientation, not a task category. In AI-rich higher education, credibility depends less on whether tasks resemble real-world practice and more on whether they generate defensible evidence of capability. The practical move is from producing authentic-looking artefacts to designing authenticated processes through which students demonstrate judgement, verification, and responsibility (Tsiligkiris, 2026).
References
Ajjawi, R., Tai, J., Dollinger, M., Dawson, P., Boud, D., & Bearman, M. (2024). From authentic assessment to authenticity in assessment: Broadening perspectives. Assessment & Evaluation in Higher Education, 49(4), 499-510. https://doi.org/10.1080/02602938.2023.2271193
Ashford-Rowe, K., Herrington, J., & Brown, C. (2014). Establishing the critical elements that determine authentic assessment. Assessment & Evaluation in Higher Education, 39(2), 205-222. https://doi.org/10.1080/02602938.2013.819566
Bearman, M., Tai, J., Dawson, P., Boud, D., & Ajjawi, R. (2024). Developing evaluative judgement for a time of generative artificial intelligence. Assessment & Evaluation in Higher Education, 49(6), 893-905. https://doi.org/10.1080/02602938.2024.2335321
Boud, D. (2000). Sustainable assessment: Rethinking assessment for the learning society. Studies in Continuing Education, 22(2), 151-167. https://doi.org/10.1080/713695728
Corbin, T., Bearman, M., Boud, D., & Dawson, P. (2026). The wicked problem of AI and assessment. Assessment & Evaluation in Higher Education, 51(4), 736-752. https://doi.org/10.1080/02602938.2025.2553340
Dawson, P., Bearman, M., Dollinger, M., & Boud, D. (2024). Validity matters more than cheating. Assessment & Evaluation in Higher Education, 49(7), 1005-1016. https://doi.org/10.1080/02602938.2024.2386662
Fawns, T., Bearman, M., Dawson, P., Nieminen, J. H., Ashford-Rowe, K., Willey, K., Jensen, L. X., Damsa, C., & Press, N. (2025). Authentic assessment: From panacea to criticality. Assessment & Evaluation in Higher Education, 50(3), 396-408. https://doi.org/10.1080/02602938.2024.2404634
Gulikers, J. T. M., Bastiaens, T. J., & Kirschner, P. A. (2004). A five-dimensional framework for authentic assessment. Educational Technology Research and Development, 52(3), 67-86. https://doi.org/10.1007/BF02504676
Kane, M. (2013). The argument-based approach to validation. School Psychology Review, 42(4), 448-457. https://doi.org/10.1080/02796015.2013.12087465
Kofinas, A. K., Tsay, C. H.-H., & Pike, D. (2025). The impact of generative AI on academic integrity of authentic assessments within a higher education context. British Journal of Educational Technology, 56(6), 2522-2549. https://doi.org/10.1111/bjet.13585
McArthur, J. (2023). Rethinking authentic assessment: Work, well-being, and society. Higher Education, 85(1), 85-101. https://doi.org/10.1007/s10734-022-00822-y
Messick, S. (1994). Alternative modes of assessment, uniform standards of validity. ETS Research Report Series, 1994(2), 1-22. https://doi.org/10.1002/j.2333-8504.1994.tb01634.x
Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A framework for ethical integration of generative AI in educational assessment. Journal of University Teaching and Learning Practice, 21(6). https://doi.org/10.53761/q3azde36
Scarfe, P., Watcham, K., Clarke, A., & Roesch, E. (2024). A real-world test of artificial intelligence infiltration of a university examinations system: A Turing Test case study. PLOS ONE, 19(6), e0305354. https://doi.org/10.1371/journal.pone.0305354
Tai, J., Ajjawi, R., Bearman, M., Boud, D., Dawson, P., & Jorre de St Jorre, T. (2023). Assessment for inclusion: Rethinking contemporary strategies in assessment design. Higher Education Research & Development, 42(2), 483-497. https://doi.org/10.1080/07294360.2022.2057451
Tsiligkiris, V. (2026). From authentic products to authenticated processes: A systematic conceptual review of authentic assessment in AI-rich higher education. Assessment & Evaluation in Higher Education. https://doi.org/10.1080/02602938.2026.2695376
Villarroel, V., Bloxham, S., Bruna, D., Bruna, C., & Herrera-Seda, C. (2018). Authentic assessment: Creating a blueprint for course design. Assessment & Evaluation in Higher Education, 43(5), 840-854. https://doi.org/10.1080/02602938.2017.1412396
Wiggins, G. (1998). Educative assessment: Designing assessments to inform and improve student performance. Jossey-Bass.