The DOI matches its Crossref record; the references it cites are not checked yet.
Transforming written assessment design to embrace AI: what needs to be changed to encourage higher-order critical thinking
Open accessAbstract
Uncritical use of generative AI (GenAI) responses is a major concern among educators as it can hinder knowledge development, creativity, critical thinking and academic misconduct. To mitigate these repercussions, current discussions predominantly focus on changing assessment methods or policing students’ use of GenAI, which greatly shapes students’ GenAI use, for better or for worse. Few studies have examined how different assessment designs impact the way students use GenAI for coursework and the quality of GenAI-assisted writing. This study uncovered the relationships between assessment design and critical thinking in students’ writing through analysing lecturer feedback on 51 postgraduate ChatGPT-assisted students’ assignments across fourteen modules and assessment information related to the assignments. Results revealed that word limits, genres, information about organisational structures, and cognitive domains required by assessments significantly determined students’ critical thinking performance in their disciplinary writing. Based on the results, we suggested (a) setting word limits based on task complexity rather than module credits, (b) designing integrated tasks with varied assessment methods to encourage critical thinking and knowledge development, (c) providing an appropriate amount of structural information to create space for critical thinking and (d) explicitly signalising cognitive domains required by assessments to address GenAI’s impact on writing. We further encourage educators to critically reflect on the existing assessment guidance and practices to design assessments that cultivate critical AI users in an AI-empowered world.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“LLMs dominate the planning phase for idea generation and outlining, continue supporting the drafting phase for content expansion, while AWE tools are reserved primarily for revision and editing (Wang, 2024; Zhao and Dang, 2026).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Zhao and Dang, 2026
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The phenomenon of “metacognitive laziness” emerges when assessment systems emphasize final products over learning processes, incentivizing students to optimize for efficiency rather than skill development (Zhao and Dang, 2026).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Zhao and Dang, 2026
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Prevents “content inflation” and uncritical reliance on AI for long outputs (Zhao and Dang, 2026).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Zhao and Dang, 2026
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This imbalance forces students to navigate complex decisions without pedagogical scaffolding, leading to situational interpretations and “functional failure” in distinguishing AI as learning tool versus learning substitute (Zhao and Dang, 2026).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Zhao and Dang, 2026
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“The absence of longitudinal data limits determination of long-term impacts on critical thinking skills (Zhao and Dang, 2026).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Zhao and Dang, 2026
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
The citation lists haven’t been fetched yet
References 46 · Cited by 4
Research works printed but not linked: 44
- Al Jaghoub, J., Suleiman, A., Takshe, A. A., Moussa, S., Gilani, S. A. M., Sheikh, S., & Tantry, A. (2024). The Role of Innovation in Waste Management for Enterprises: A Critical Review of the Worldwide Literature. In R. El Khoury (Ed.), Technology-Driven Business Innovation: Unleashing the Digital Advantage, Volume 1 (pp. 453–464). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-51997-0_38 · DOI
- Andrade, H. (2000). Using rubrics to promote thinking and learning. Educational Leadership, 57(5), 13–18.
- Barrett, A., & Pack, A. (2023). Not quite eye to A.I.: Student and teacher perspectives on the use of generative artificial intelligence in the writing process. International Journal of Educational Technology in Higher Education, 20(1), 59. https://doi.org/10.1186/s41239-023-00427-0 · DOI · Paper page
- Bean, J. C., & Melzer, D. (2021). Engaging Ideas: The Professor’s Guide to Integrating Writing, Critical Thinking, and Active Learning in the Classroom. Wiley.
- Bearman, M., Dawson, P., Boud, D., Bennett, S., Hall, M. and Molloy, E. (2016 ). Support for assessment practice: developing the Assessment Design Decisions Framework. Teaching in higher education. 21(5), pp.545–556. · DOI
- Beckingham, S., Lawrence, J., Powell, S., & Hartley, P. (2024). Using generative AI effectively in higher education: Sustainable and ethical practices for learning, teaching and assessment. Routledge. https://doi.org/10.4324/9781003482918 · DOI
- Borji, A. (2023) A Categorical Archive of ChatGPT Failures. https://doi.org/10.48550/arXiv.2302.03494} · DOI · Paper page
- Cohen, J. (1988). Statistical power analysis for the behavioral sciences (Second edition. ed.). L. Erlbaum Associates.
- Cordero, J., Torres-Zambrano, J., & Cordero-Castillo, A. (2025). Integration of generative artificial intelligence in higher education: Best practices. Education Sciences, 15(1), 32. https://doi.org/10.3390/educsci15010032 · DOI · Paper page
- Coughlan, T., & Iniesto, F. (2025). What should I know? Analysing behaviour and feedback from student use of a virtual assistant to share information about disabilities. The Internet and Higher Education, 66, 101002. https://doi.org/10.1016/j.iheduc.2025.101002 · DOI
- Deane, P. (2011). Writing assessment and cognition. ETS Research Report Series, 2011(1), i–60. https://doi.org/10.1002/j.2333-8504.2011.tb02250.x. https://doi.org/https://doi. · DOI
- Elbow, P. (2007). Reconsiderations: Voice in writing Again–Embracing contraries. College English, 70(2), 168–188. https://doi.org/10.58680/ce20076342 · DOI
- Evans, C. and Harrington, P. (2024). Applying Scalability to Meet Word Count Requirements in Written Assessments. College teaching, pp.1–8. · DOI
- Gernal, L., Tantry, A., Gilani, S. A. M., & Peel, R. (2024). The Impact of Online Learning and Soft Skills on College Student Satisfaction and Course Feedback. In R. El Khoury (Ed.), Technology-Driven Business Innovation: Unleashing the Digital Advantage, Volume 1 (pp. 515–528). Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-51997-0_44 · DOI
- Gibbons, J. D and Chakraborti, S. (2020). Nonparametric Statistical Inference [Online]. CRC Press. · DOI
- Gilani, S. A. M., Copiaco, A., Gernal, L., Yasin, N., Nair, G., & Anwar, I. (2023). Savior or distraction for survival: Examining the applicability of machine learning for rural family farms in the united Arab Emirates. Sustainability, 15(4), 3720. https://www.mdpi.com/2071-1050/15/4/3720 · DOI
- Gilani, S. A. M., Tantry, A., Askri, S., Gernal, L., Sergio, R., & Mataruna-Dos-Santos, L. J. (2024). 2024//). Adoption of Machine Learning by Rural Farms: A Systematic Review. Computing and Informatics.
- Jiang, Y. (2025). Interaction and dialogue: Integration and application of artificial intelligence in blended mode writing feedback. The Internet and Higher Education, 64, 100975. https://doi.org/10.1016/j.iheduc.2024.100975 · DOI
- Kim, J., Yu, S., Detrick, R., & Li, N. (2025). Exploring students’ perspectives on generative AI-assisted academic writing. Education and Information Technologies, 30(1), 1265–1300. https://doi.org/10.1007/s10639-024-12878-7 · DOI · Paper page
- Kitamura, F. C. (2023). ChatGPT is shaping the future of medical writing but still requires human judgment. Radiology, 307(2), e230171. https://doi.org/10.1148/radiol.230171 · DOI · Paper page
- Laflamme, A.S. and Bruneault, F. (2025). Redefining Academic Integrity in the Age of Generative Artificial Intelligence: The Essential Contribution of Artificial Intelligence Ethics. Journal of scholarly publishing. 56(2), pp.481–509. · DOI
- Lee, S. S., Li, N., & Kim, J. (2024). Conceptual model for Mexican teachers’ adoption of learning analytics systems: The integration of the information system success model and the technology acceptance model. Education and Information Technologies, 29(11), 13387–13412. https://doi.org/10.1007/s10639-023-12371-7 · DOI
- Li, Y., Sha, L., Yan, L., Lin, J., Raković, M., Galbraith, K., Lyons, K., Gašević, D., & Chen, G. (2023). Can large Language models write reflectively. Computers and Education: Artificial Intelligence, 4, 100140. https://doi.org/10.1016/j.caeai.2023.100140 · DOI · Paper page
- Maranna, S., Claassen, A., Joksimovic, S., Willison, J., Parange, N., & Costabile, M. (2025). Cognitive presence and self-regulated learning: Learning transfer in an online allied health course. Journal of University Teaching and Learning Practice, 22(1). https://doi.org/10.53761/h2gnev81 · DOI
- Martín-Raugh, M., Kell, H., Ling, G., Fishtein, D., & Yang, Z. (2023). Noncognitive skills and critical thinking predict undergraduate academic performance. Assessment & Evaluation in Higher Education, 48(3), 350–361. https://doi.org/10.1080/02602938.2022.2073964 · DOI
- Merine, R., & Purkayastha, S. (2022). 11–14 June 2022). Risks and Benefits of AI-generated Text Summarization for Expert Level Content in Graduate Health Informatics. 2022 IEEE 10th International Conference on Healthcare Informatics (ICHI). · DOI · Paper page
- Nicol, D. J., & Macfarlane-Dick, D. (2006). Formative assessment and self‐regulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. https://doi.org/10.1080/03075070600572090 · DOI · Paper page
- Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4. https://doi.org/10.1207/S15326985EP3801_1 · DOI · Paper page
- Paul, R., & Elder, L. (2014a). Critical thinking: Tools for taking charge of your learning and your life (Third edition. ed). Pearson.
- Paul, R., & Elder, L. (2014b). The Miniature Guide to Critical Thinking Concepts and Tools. In (Seventh Edition ed.).
- Raz, T., Reiter-Palmon, R., & Kenett, Y. N. (2024). Open and closed-ended problem solving in humans and AI: The influence of question asking complexity. Thinking Skills and Creativity, 53, Article 101598. https://doi.org/10.1016/j.tsc.2024.101598 · DOI
- Robinson, P. (2001). Task complexity, task difficulty, and task production: Exploring interactions in a componential framework. Applied Linguistics, 22(1), 27–57. https://doi.org/10.1093/applin/22.1.27 · DOI · Paper page
- Sinclair, J. (1991). Corpus, concordance, collocation. Oxford University Press.
- Skehan, P. (1998). A cognitive approach to language learning. Oxford University Press.
- Skehan, P. (2009). Modelling second language performance: Integrating Complexity, Accuracy, Fluency, and lexis. Applied Linguistics, 30(4), 510–532. https://doi.org/10.1093/applin/amp047 · DOI · Paper page
- Swales, J.M. and Feak, C.B. (2012). Academic writing for graduate students : essential tasks and skills 3rd edition. Ann Arbor: The University of Michigan Press. · DOI
- The University of Reading (2024). Focus on: Assessment & Feedback - Assignment briefs. In U. o. Reading (Ed.).
- Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press.
- Wingate, U. (2012). Argument!’ helping students understand what essay writing is about. Journal of English for Academic Purposes, 11(2), 145–154. https://doi.org/10.1016/j.jeap.2011.11.001 · DOI
- Yasin, N., Gilani, S. A. M., Contu, D., & Fayaz, M. J. (2022a). Simulation-based Learning in Business and Entrepreneurship in Higher Education: A Review of the Games Available. In D. Hyams-Ssekasi & N. Yasin (Eds.), Technology and Entrepreneurship Education: Adopting Creative Digital Approaches to Learning and Teaching (pp. 25–51). Springer International Publishing. https://doi.org/10.1007/978-3-030-84292-5_2 · DOI
- Yasin, N., Majid Gilani, S. A., & Nair, G. (2022b). Dump the paper quiz”—The PERI model for exploring gamification in student learning in the United Arab Emirates. Industry and Higher Education, 36(5), 623–637. https://doi.org/10.1177/09504222211055067 · DOI
- Zhao, H. (2024). Promoting accessibility of assessment criteria: Shifting from a product- to a process- and future-oriented approach. Teaching in Higher Education, 29(5), 1283–1301. https://doi.org/10.1080/13562517.2022.2129964 · DOI
- Zhao, H., Dang, T. N. Y., & Finlayson, N. (2024). Operationalising critical thinking in postgraduate disciplinary writing: Insights from corpus and cluster analyses of lecturer feedback. Assessment and Evaluation in Higher Education. https://doi.org/10.1080/02602938.2024.2406869 · DOI
- Zhao, H., Dang, T. N. Y., & Finlayson, N. (2025). Education lecturers’ expectations about writing proficiency: Insights from corpus analysis of teacher feedback on academic writing. Journal of Second Language Writing, 67, Article 101173. https://doi.org/10.1016/j.jslw.2024.101173 · DOI
Metadata
- Authors
- Zhao, H., & Dang, T. N. Y.
- Affiliations
- University of Leeds (GB)
- Corresponding author
- Zhao, Huahui
- Venue
- Education and Information Technologies
- Published
- 2026-01-21
- Volume
- 31
- Issue
- 7
- Pages
- 1909-1931
- ISSN
- 1360-2357 (ISSN-L)
- 1573-7608
- OpenAlex
- W7125121528
- Cited by
- 4
- References
- 46
- Citations by year
- 2026: 4
- FWCI
- 34.03
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Education and Information Technologies (publishedVersion, cc-by)
- White Rose Research Online (University of Leeds, The University of Sheffield, University of York) (acceptedVersion)
- Funders
- British Council (GES_2312)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Student Assessment and Feedback
- Social Sciences › Education › Academic integrity and plagiarism
- Keywords
- generative AI, ChatGPT, assessment design, critical thinking, higher-order thinking skills, AI-assisted writing, word limits, cognitive domain, graduate students, instructor feedback, academic misconduct, task complexity, writing genres
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
