Designing Assessments for Authentic Human Thinking: A Systematic Review of Assessment Frameworks Amid Generative Artificial Intelligence

  • Ntombentle Nomhlangano Grace Zoko-Fumba Walter Sisulu University, Private Bag X1, Neslo Mandela Drive, Mthatha, 5117. Eastern Cape,  South Africa
DOI: https://doi.org/10.51169/ideguru.v11i2.2579

Abstract

The rapid advancement of generative artificial intelligence (GenAI) has transformed higher education by enabling students to produce sophisticated academic outputs with unprecedented ease, raising concerns about the validity and authenticity of traditional assessment practices. Grounded in Situated Cognition Theory, this systematic review aimed to identify and synthesise contemporary assessment frameworks that support authentic human thinking in higher education amid the widespread adoption of GenAI. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) framework, the review conducted a systematic search of Web of Science, ERIC, and Google Scholar, resulting in 18 studies for thematic synthesis. Findings indicate a paradigm shift from assessing knowledge products to evaluating the cognitive processes underpinning learning. Three interconnected dimensions of authentic human thinking emerged: subjective synthesis, metacognitive dialogue, and situated judgement. The review proposes a conceptual framework that integrates these dimensions to inform the redesign of assessments in AI-mediated learning environments. The framework provides practical guidance for lecturers, curriculum designers, institutional leaders, and policymakers seeking to develop valid, credible, and educationally meaningful assessment practices.

Keywords: generative artificial intelligence, authentic human thinking, authentic assessment, assessment redesign, situated cognition theory

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Author Biography

Ntombentle Nomhlangano Grace Zoko-Fumba, Walter Sisulu University, Private Bag X1, Neslo Mandela Drive, Mthatha, 5117. Eastern Cape

Ntombentle Nomhlangano Grace Zoko-Fumba is a PhD candidate and a lecturer at iYunivesithi Walter Sisulu, South Africa, specialising in Commerce Education and ICT integration in teaching and learning. She holds qualifications in Computer-Based Education (CBE), Commercial Law and Business Management, and Education. Her research interests include career development, higher education, educational technology, and innovative teaching, learning, and assessment practices.

Published
2026-10-02
How to Cite:
Zoko-Fumba, N. N. G. (2026). Designing Assessments for Authentic Human Thinking: A Systematic Review of Assessment Frameworks Amid Generative Artificial Intelligence. Ideguru: Jurnal Karya Ilmiah Guru, 11(2), 208-215. https://doi.org/10.51169/ideguru.v11i2.2579
Issue
Vol. 11 No. 2 (2026): May 2026 Edition
Section
Literature Review

Copyright (c) 2026 Ntombentle Nomhlangano Grace Zoko-Fumba

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

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References

Afzal, A., Rafiq, S., & Sami, A. (2026). Faculty readiness and ethical perceptions of artificial intelligence integration in higher education teaching and assessment. Higher Learning Research Communications, 16(1), 1–19. https://doi.org/10.18870/hlrc.v16i1.1742

Agbo, F. J., Olivia, C., Oguibe, G., Sanusi, I. T., & Sani, G. (2025). Computing education using generative artificial intelligence tools: A systematic literature review. Computers and Education Open, 9, 100266. https://doi.org/10.1016/j.caeo.2025.100266

Arslan, B., Lehman, B., Tenison, C., Sparks, J. R., López, A. A., Gu, L., & Zapata-Rivera, D. (2024). Opportunities and challenges of using generative AI to personalize educational assessment. Frontiers in Artificial Intelligence, 7, Article 1460651. https://doi.org/10.3389/frai.2024.1460651

Atchley, P., Pannell, H., Wofford, K., Hopkins, M., & Atchley, R. A. (2024). Human and AI collaboration in the higher education environment: Opportunities and concerns. Cognitive Research: Principles and Implications, 9(1), Article 20. https://doi.org/10.1186/s41235-024-00547-9

Ayoo, S. N., Tovey, T. L. S., Clark, T., Huff, S., Somo-Aina, O., Moller, J. R., Yan, X., Chuquiruna, J. A., Odetola, C., Reid, A., & Boyce, A. S. (2026). Producing competent evaluators: A self-study of a graduate evaluation program. American Journal of Evaluation, 47(2), 191–217. https://doi.org/10.1177/10982140261425204.

Batista, J., Mesquita, A., & Carnaz, G. (2024). Generative AI and higher education: Trends, challenges, and future directions from a systematic literature review. Information, 15(11), 676. https://doi.org/10.3390/info15110676.

Bearman, M., Ajjawi, R., Boud, D., Tai, J., & Dawson, P. (2024). Assessment reform for the age of artificial intelligence (Lodge, J. M., Howard, S., Bearman, M., Dawson, P., & Associates, 2023). TEQSA discussion paper / related CRADLE materials.

Belkina, M., Daniel, S. A., Nikolic, S., Haque, R., Lyden, S., Neal, P., Grundy, S., & Hassan, G. M. (2025). Implementing generative AI (GenAI) in higher education: A systematic review of case studies. Computers and Education: Artificial Intelligence, 8, 100407. https://doi.org/10.1016/j.caeai.2025.100407.

Braun, V. & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in Psychology. 3(2), 77-101, https://doi.org/10.1191/1478088706qp063a

Chiu, T. K. F. (2024). Future research recommendations for transforming higher education with generative AI. Computers and Education: Artificial Intelligence, 6, 100197. httpassessment s://doi.org/10.1016/j.caeai.2023.100197.

Francis, N. J., Jones, S., & Smith, D. P. (2025). Generative AI in higher education: Balancing innovation and integrity. British Journal of Biomedical Science, 81, 14048. https://doi.org/10.3389/bjbs.2024.14048.

Kofinas, A. K., Tsay, C.-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, 2522–2549. https://doi.org/10.1111/bjet.13585.

Liu, X., & Zhong, B. (2025). Integrating generative artificial intelligence into student learning: A systematic review from a TPACK perspective. Educational Research Review, 49, 100741. https://doi.org/10.1016/j.edurev.2025.100741.

Nikolic, S., Sandison, C., Haque, R., Daniel, S., Grundy, S., Belkina, M., Lyden, S., Hassan, G. M., & Neal, P. (2024). ChatGPT, Copilot, Gemini, SciSpace and Wolfram versus higher education assessments: An updated multi-institutional study of the academic integrity impacts of generative artificial intelligence (GenAI) on assessment, teaching and learning in engineering. Australian Journal of Engineering Education, 29(2), 126–153. https://doi.org/10.1080/22054952.2024.2372154

Perkins, M., Furze, L., Roe, J., & MacVaugh, J. (2024). The Artificial Intelligence Assessment Scale (AIAS): A framework for rethinking assessment in the age of generative artificial intelligence. Computers and Education: Artificial Intelligence, 6, 100213.

Perkins, M., Roe, J., & Furze, L. (2025). Reimagining the Artificial Intelligence Assessment Scale (AIAS): A refined framework for educational assessment. Journal of University Teaching and Learning Practice, 22(7). https://doi.org/10.53761/rrm4y757

Rodríguez-Ortiz, M., Santana-Mancilla, P., & Anido-Rifón, L. (2025). Machine learning and generative AI in learning analytics for higher education: A systematic review of models, trends, and challenges. Applied Sciences, 15(15), 8679. https://doi.org/10.3390/app15158679

Sannholm, R., & Risku, H. (2024). Situated minds and distributed systems in translation: Exploring the conceptual and empirical implications. Target. International Journal of Translation Studies. https://doi.org/10.1075/target.22172.san

Schmidt, L., Finnerty Mutlu, A. N., Elmore, R., Olorisade, B. K., Thomas, J., & Higgins, J. P. T. (2021). Data extraction methods for systematic review (semi)automation: Update of a living systematic review. F1000Research, 10, 401. https://doi.org/10.12688/f1000research.51117.3

Sokhanvar, Z., Salehi, K., & Sokhanvar, F. (2021). Advantages of authentic assessment for improving the learning experience and employability skills of higher education students: A systematic literature review. Studies in Educational Evaluation, 70, 101030.

Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. F. (2024). A scoping review on how generative artificial intelligence transforms assessment in higher education. International Journal of Educational Technology in Higher Education, 21, 40. https://doi.org/10.1186/s41239-024-00468-z

Yan, L., Greiff, S., Teuber, Z., & Gašević, D. (2024). Promises and challenges of generative artificial intelligence for human learning. Nature Human Behaviour, 8, 1839–1850. https://doi.org/10.1038/s41562-024-02004-5