Race with the machines: assessing the capability of generative AI in solving authentic assessments
Open accessAbstract
In this study, we introduce a framework designed to help educators assess the effectiveness of popular generative artificial intelligence (AI) tools in solving authentic assessments. We employed Bloom’s taxonomy as a guiding principle to create authentic assessments that evaluate the capabilities of generative AI tools. We applied this framework to assess the abilities of ChatGPT-4, ChatGPT-3.5, Google Bard and Microsoft Bing in solving authentic assessments in economics. We found that generative AI tools perform very well at the lower levels of Bloom's taxonomy while still maintaining a decent level of performance at the higher levels, with “create” being the weakest level of performance. Interestingly, these tools are better able to address numeric-based questions than text-based ones. Moreover, all the generative AI tools exhibit weaknesses in building arguments based on theoretical frameworks, maintaining the coherence of different arguments and providing appropriate references. Our study provides educators with a framework to assess the capabilities of generative AI tools, enabling them to make more informed decisions regarding assessments and learning activities. Our findings demand a strategic reimagining of educational goals and assessments, emphasising higher cognitive skills and calling for a concerted effort to enhance the capabilities of educators in preparing students for a rapidly transforming professional environment. Implications for practice or policy Our proposed framework enables educators to systematically evaluate the capabilities of widely used generative AI tools in assessments and assist them in the assessment design process. Tertiary institutions should re-evaluate and redesign programmes and course learning outcomes. The new focus on learning outcomes should address the higher levels of educational goals of Bloom’s taxonomy, specifically the “create” level.
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Generative AI performs weakest at Bloom's create level and struggles with coherent argumentation.
“Generative AI performs weakest at Bloom’s create level and struggles with coherent argumentation (Thanh et al., 2023), though intentional pedagogical design can support deeper engagement (Kwan et al., 2025; Zhou et al., 2024).”
From Reimagining writing assessment for the AI era: a systematic review on balancing AI support and authentic skill growth (2026) · cites this paper as Thanh et al., 2023
Assessments should prioritise critical thinking, problem-solving, and creativity over rote memorisation.
“Research suggests that assessments should prioritise critical thinking, problemsolving, and creativity over rote memorisation [ 26, 34].”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [34]
Generative AI tools such as ChatGPT-4, ChatGPT-3.5, Google Bard, and Microsoft Bing perform well at the lower levels of Bloom's taxonomy (e.g., remembering and understanding).
“A study by [ 34] found that generative AI tools, such as ChatGPT-4, Chat -GPT-3.5, Google Bard, and Microsoft Bing, perform well at the lower levels of Bloom's taxonomy (e.g., remember -ing and understanding).”
From Redefining student assessment in AI-infused learning environments: a systematic review of challenges and strategies for academic integrity (2026) · cites this paper as [34]
Integrating multimodal generative models can enhance the authenticity of simulated assessment environments.
“Integrating multimodal generative models, such as GPT-4 Vision for text and image generation, Meta's Voicebox for audio creation from text, and generative adversarial networks for digital avatar production, can further enhance the authenticity of simulated assessment environments [66].”
From Promises and challenges of generative artificial intelligence for human learning (2024) · cites this paper as [66]
The Introduction discusses how generative AI can complete authentic assessment tasks, and the literature review describes realism in assessments. Neither addresses using multimodal models to make simulated assessment environments more authentic. Searches for simulated environments, GPT-4 Vision, Voicebox, audio, and avatars found no supporting passage in our copy. (LLM, conf=0.74)
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The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI supported assessment
Furze, L., Perkins, M., Roe, J., & MacVaugh, J. (2024) · Australasian Journal of Educational Technology · doi:10.14742/ajet.9434 · Cited by 92 · see citing works
Zhang, X., Zhang, P., Shen, Y., Liu, M., Wang, Q., Gašević, D., & Fan, Y. (2024) · Frontiers of Digital Education · doi:10.1007/s44366-024-0028-5 · Cited by 83 · see citing works
Enhancing academic integrity among students in GenAI Era:A holistic framework
Rasul, T., Nair, S., Kalendra, D., Balaji, M. S., Santini, F. D. O., Ladeira, W. J., Rather, R. A., Yasin, N., Rodriguez, R. V., Kokkalis, P., et al. (2024) · The International Journal of Management Education · doi:10.1016/j.ijme.2024.101041 · Cited by 82 · see citing works
AI-resistant assessments in higher education: Practical insights from faculty training workshops
Awadallah Alkouk, W., & Khlaif, Z. N. (2024) · Frontiers in Education · doi:10.3389/feduc.2024.1499495 · Cited by 68 · see citing works
Lubbe, A., Marais, E., & Kruger, D. J. D. (2025) · Education and Information Technologies · doi:10.1007/s10639-025-13476-x · Cited by 90 · see citing works
ChatGPT in the classroom: evaluating its role in fostering critical evaluation skills
Oates, A., & Johnson, D. (2025) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00452-8 · Cited by 61 · see citing works
“This approach has also been used in the context of authentic assessments in eco - nomics education (Nguyen Thanh et al., 2023 ).”
Reimagining Higher Education: Navigating the Challenges of Generative AI Adoption
Hughes, L., Malik, T., Dettmer, S., Al-Busaidi, A. S., & Dwivedi, Y. K. (2025) · Information Systems Frontiers · doi:10.1007/s10796-025-10582-6 · Cited by 83 · see citing works
Swindell, A., Greeley, L., Farag, A., & Verdone, B. (2024) · Online Learning · doi:10.24059/olj.v28i2.4438 · Cited by 45 · see citing works
“Bloom’s Taxonomy (Armstrong, 2010) Given the utility that the RBT provides in examining learning processes, research has already been conducted on AI and the RBT. Research on generative AI Large Language Models (LLM), like ChatGPT, have shown these LLMs can complete activities across the RBT, though they perform signi…”
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Bikanga Ada, M. (2024) · Education Sciences · doi:10.3390/educsci14101106 · Cited by 40 · see citing works
Ma, T. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100336 · Cited by 54 · see citing works
A Meta‐Analysis of the Impact of Generative Artificial Intelligence on Learning Outcomes
Ma, N., & Zhong, Z. (2025) · Journal of Computer Assisted Learning · doi:10.1111/jcal.70117 · Cited by 57 · see citing works
Ulla, M. B., Advincula, M. J. C., Mombay, C. D. S., Mercullo, H. M., Nacionales, J. P., & Entino-Señorita, A. D. (2024) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2024.100314 · Cited by 45 · see citing works
Mata, O., Ponce, P., Pérez, C., Ramirez, M., Anthony, B., Bradley, R., Apte, P. P., MacCleery, B., & Molina, A. (2025) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02583-8 · Cited by 33 · see citing works
AI in Tertiary Education: Progress on Research and Practice
Thompson, K., Corrin, L., & Lodge, J. M. (2023) · Australasian Journal of Educational Technology · doi:10.14742/ajet.9251 · Cited by 31 · see citing works
“…of generative AI for learning in a global context through an engineering course in South-east Asia (Pham et al., 2023), students’ experiences with ChatGPT in online courses in Turkey (Kayali et al., 2023) and its use in teaching in universities in China and Vietnam (Li, 2023; Thanh et al., 2023).”
BackgroundNcube, P. D. N., Dzvapatsva, G. P., Matobobo, C., & Ranga, M. M. (2026) · AI and Ethics · doi:10.1007/s43681-025-00871-w · Cited by 31 · see citing works
“A study by [ 34] found that generative AI tools, such as ChatGPT-4, Chat - GPT-3.”
The Education Leadership Challenges for Universities in a Postdigital Age
Ellis, R. A. (2024) · Postdigital Science and Education · doi:10.1007/s42438-024-00461-9 · Cited by 29 · see citing works
Xiao-hua, L. (2024) · The Asia-Pacific Education Researcher · doi:10.1007/s40299-024-00890-x · Cited by 31 · see citing works
“For instance, recent studies by Raftery (2023) and Thanh et al. (2023) demonstrated the remarkable performance of latest GenAI tools such as ChatGPT-4 in solving different types of quizzes and problems, especially questions requiring lower-order skills, posing tremendous challenges to assessments…”
Reconsidering the Use of Oral Exams and Assessments: An Old Way to Move Into a New Future
Fenton, A. G. (2025) · Educational Researcher · doi:10.3102/0013189x251333638 · Cited by 22 · see citing works
“Generative AI tools have been found to perform well at the lower levels of Bloom’s taxonomy but struggle at the create level and making arguments built on theoretical frameworks (Nguyen et al., 2023).”
Reimer, E. C. (2024) · Social Sciences · doi:10.3390/socsci13120648 · Cited by 18 · see citing works
Peters, M., & Angelov, D. (2025) · International Journal for Educational Integrity · doi:10.1007/s40979-025-00201-x · Cited by 27 · see citing works
“With the arrival of GenAI in education, many experts are urging lecturers to modify their assessment methods to try and curb the temptation for students to include GenAI-generated text in their assignments (Dakakni and Safa 2023; Nguyen Thanh et al. 2023; Perkins et al. 2023).”
Prompting for creative problem-solving: A process-mining study
Urban, M., Lukavský, J., Brom, C., Hein, V., Svacha, F., Děchtěrenko, F., & Urban, K. (2025) · Learning and Instruction · doi:10.1016/j.learninstruc.2025.102156 · Cited by 15 · see citing works
Generative AI in Accounting Education: Evaluating ChatGPT's Role in Assessment and Skill Development
Tharapos, M., Lau, K. H., Peszynski, K. J., Nguyen, L. A., Magdziarz, S., Morton, E., Borg, V., & Duan, S. X. (2025) · Accounting and Finance · doi:10.1111/acfi.70051 · Cited by 13 · see citing works
Reihanian, I., Hou, Y., Chen, Y., & Zheng, Y. (2025) · Communications in computer and information science · doi:10.1007/978-3-031-94943-2_11 · Cited by 12 · see citing works
AI Facilitated Critical Thinking in an Undergraduate Project Based Service-Learning Course
Kimmel, S. B. (2024) · Journal of Behavioral and Applied Management · doi:10.21818/001c.122149 · Cited by 12 · see citing works
Raghu Raman, R., Achuthan, K., & Nedungadi, P. (2026) · Information · doi:10.3390/info17030241 · Cited by 15 · see citing works
Matijević, M. M., Pisker, B., & Đokić, K. (2024) · Social Sciences · doi:10.3390/socsci13090479 · Cited by 8 · see citing works
Rethinking Educational Assessment in the Age of Artificial Intelligence
Khlaif, Z. N. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-7255-5.ch005 · Cited by 7 · see citing works
Applying the AI assessment scale in writing and translation for EFL
Roe, J., Perkins, M., & Furze, L. (2025) · Artificial Intelligence in Education · doi:10.1108/aiie-02-2025-0034 · Cited by 20 · see citing works
“The view that assessment in higher education must be re-evaluated has now received widespread attention in the literature (Bearman et al., 2024; Mao et al., 2024; Rasul et al., 2024; Thanh et al., 2023; Thompson et al., 2023; Xia et al., 2024).”
Royse, E. A., Manzanares, A. D., Wang, H., Haudek, K. C., Azzarello, C. B., Horne, L., Druckenbrod, D. L., Shiroda, M., Adams, S. R., Fairchild, E., et al. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-03499-z · Cited by 5 · see citing works
“Some studies have found that generative AI models still perform less well for producing more complex assessment tasks and tend to do better on quantitative tasks as compared to explanatory (Nguyen Thanh et al., 2023).”
Pradeesh, N., Thushara, M. G., Krishna, K. A., Pranav, V. S., & Krishnamoorthy, S. (2025) · IEEE Access · doi:10.1109/access.2025.3638427 · Cited by 6 · see citing works
“Techniques such as chain-of-thought prompting further align assessments with cognitive levels outlined in Bloom’s Taxonomy [28].”
Wani, T. A., Liem, M. K., Prasad, N., Robinson, K. M., Nexhip, A., Tassos, M., Gjorgioski, S., Khan, U. R., Boyd, J., & Riley, M. M. (2026) · JMIR Medical Education · doi:10.2196/82988 · Cited by 4 · see citing works
Castro, M. P., & Castillo-Martínez, I. M. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1729156 · Cited by 3 · see citing works
Educational Technology and Responsible Automated Essay Scoring in the Generative AI Era
Trung, H. T., Nguyen, M. N., Nguyen, T. T. H., Vo, D. T. H., Thanh, B. N., Nguyen, K., Ha, S., & Le, T. V. A. (2024) · Practice, progress, and proficiency in sustainability · doi:10.4018/979-8-3693-2827-9.ch011 · Cited by 3 · see citing works
Self-citationUniversity Student Readiness and Academic Integrity in Using ChatGPT and AI Tools for Assessments
Chuah, K.-M., & Sumintono, B. (2024) · Lecture notes in educational technology · doi:10.1007/978-981-97-6136-4_4 · Cited by 2 · see citing works
Felipe, A. L., Khwakhali, U. S., & Nguyen, T. N. (2025) · International STEM Education Conference (iSTEM-Ed) · doi:10.1109/istem-ed65612.2025.11129352 · Cited by 2 · see citing works
“The authors in [30] introduced a framework that helps educators assess the effectiveness of Gen-AI in solving authentic assessments.”
Lee, H.-S., Chang, C.-C., Chen, C.-Y., & Hsu, Y.-H. (2025) · The Electronic Library · doi:10.1108/el-04-2025-0136 · Cited by 2 · see citing works
Multimodal generative AI for automated pavement condition assessment: Benchmarking model performance
Xu, C., Shu, L., Dao, A., & Cui, Y. (2026) · PLoS ONE · doi:10.1371/journal.pone.0340380 · Cited by 2 · see citing works
“Recent advancements in GAI have opened new avenues for automating assessment processes across various domains [52,53].”
Educator–GenAI Partnership Model for Assessment Design to Foster Higher-Order Thinking
Kadel, R., Zou, Z., Shailendra, S., Saxena, U. R., Sharma, A., & Islam, M. T. (2026) · Education Sciences · doi:10.3390/educsci16050672 · Cited by 2 · see citing works
Son, H. X., Nguyen, T. M., Vo, H. K., Dang, K. T., Gia, K. H., Tran, N. B., Khanh, B. L., & Nguyen, N. T. K. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-57402-3_10 · Cited by 1 · see citing works
Ethical Integration of Generative AI in Higher Education Assessments
AlMohsen, F. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-7332-3.ch017 · Cited by 1 · see citing works
Foundations of AI in Educational Assessment
Trajkovski, G. P., & Hayes, H. (2025) · Digital education and learning · doi:10.1007/978-3-031-88252-4_1 · Cited by 1 · see citing works
Emerging Trends and Big Ideas for the Future of Artificial Intelligence (AI) in Education
Venkateswaran, P. S., Kamatchi, S. D., & Ravikumar, V. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1827-1.ch012 · Cited by 1 · see citing works
De corrector a mentor: el docente ante la inteligencia artificial generativa
Pérez, A. A. J. (2025) · Revista Multidisciplinaria Voces de América y el Caribe · doi:10.69821/remuvac.v2i2.230 · Cited by 1 · see citing works
Chen, Y., & Nunes, B. P. (2025) · IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt64023.2025.00118 · Cited by 2 · see citing works
“Thanh et al. [30] introduce an evaluation framework to assess GenAI tools’ effectiveness in student assessment, guiding educators in creating taxonomy-aligned questions with corresponding rubrics.”
Shanahan, C., & Parker, L. (2025) · Information systems engineering and management · doi:10.1007/978-3-032-05306-0_14 · Cited by 1 · see citing works
Aydemir, M., & Başman, M. (2026) · Springer eBooks · doi:10.1007/978-981-95-4871-2_10 · Cited by 1 · see citing works
Nyagoga, L. M., Malik, L., Lukoko, H. O., & Liu, M. (2026) · Artificial Intelligence in Education · doi:10.1108/aiie-02-2025-0029 · Cited by 1 · see citing works
University Students’ Perceptions of Artificial Intelligence-Based Tools Beyond Text Generation
Marchetti, E., Frandsen, T. F., & Sandu, D.-A. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-13153-9_4 · Cited by 1 · see citing works
Cherner, T. S., & Donnelly, M. (2026) · Education Sciences · doi:10.3390/educsci16050706 · Cited by 2 · see citing works
An analysis of AI ‐enabled authentic assessment strategy using text mining techniques
Ogunleye, B., Olayinka, O., Zakariyyah, K. I., Ajao, O., & Sharma, H. (2026) · Review of Education · doi:10.1002/rev3.70206 · Cited by 1 · see citing works
Kumar, A., Wongsirichot, T., & Nanthaamornphong, A. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100677 · Cited by 1 · see citing works
Competence Frameworks for Exploring Generative AI in Education
Burneo, P. S., Costa, A. P., de Pinho, I. G., Muniz, A. B. X., & Moresi, E. A. D. (2025) · CABI eBooks · doi:10.1079/9781800626607.0015 · Cited by 2 · see citing works
Generative AI in International Management Education From a Consulting PerspectivePreprint
Busch, R., & Busch, M. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4791750 · Cited by 1 · see citing works
Beyond unstructured use: scaffolded integration of generative AI in assessment for learning
Cochrane, D., Littschwager, S., Tsang, E., & Jin, Y. (2026) · Cogent Education · doi:10.1080/2331186x.2026.2731708
Abdellatif, M. S., Alshehri, M. A., Lamouchi, A., Rahmath, M., & Nemt-allah, M. A. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1809174
Gambino, A. (2024) · Advances in educational technologies and instructional design book series · doi:10.4018/979-8-3693-1001-4.ch007
Authentic group projects for upper-level economics courses
Picault, J. (2025) · Citizenship Social and Economics Education · doi:10.1177/14788047251343791
Dau, L. (2025) · Journal of Knowledge Learning and Science Technology · doi:10.60087/jklst.v4.n2.001
“This aligns with Thanh et al. (2023), who noted that Generative AI tools excel at lower levels of Bloom’s taxonomy (e.g., recall and understanding) but struggle with more complex tasks, particularly those requiring creativity and critical analysis.”
Automatic Distractor and Feedback Generation in Online AI Education: A Design-Based Research Study
Liu, Z. (2025) · ACM Conference on International Computing Education Research (ICER) · doi:10.1145/3702653.3744306
Evans, S. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-6960-0.ch008
Universidad Politécnica Salesiana, E., Llerena-Izquierdo, J., Ayala-Carabajo, R., & Universidad Politécnica Salesiana, E. (2025) · doi:10.17163/abyaups.141.4
Halder, A. N., Sultana, S., Hasan, R., & Ahmed, S. (2025) · Indian Journal of Marketing · doi:10.17010/ijom/2025/v55/i10/175611
Ranatunga, S., Jayasundara, R., & Jayasinghe, B. (2025) · Moratuwa Engineering Research Conference (MERCon) · doi:10.1109/mercon67903.2025.11217133
“The refined ability to convey complex information not only expedites project workflows but also augments the exchange of ideas and solutions, thereby fostering a more cohesive and agile IT ecosystem [6].”
Transforming Creative Problem-Solving with Generative AI Tools
Urban, M., & Urban, K. (2025) · Signals and communication technology · doi:10.1007/978-3-031-96855-6_17 · Cited by 2 · see citing works
Work Engagement: Global Trends, Bibliometric Analysis (2020-2025)
Shah, N. M. (2026) · EDUCATUM Journal of Social Sciences · doi:10.37134/ejoss.vol12.sp.14.2026
Measuring GenAI Usage Patterns in a University Campus via Network Traffic Analysis
Lyu, M., Wang, Y., & Sivaraman, V. (2024) · Asian Internet Engineering Conference · doi:10.1145/3674213.3674214
Konstantinidis, K., Apostolakis, I., Malamateniou, C., & Papagiannis, P. (2025) · European Journal of Engineering and Technology Research · doi:10.24018/ejeng.2025.1.cie.70024
Yoo, T.-A., Yang, H.-S., Kim, M., & Chung, J. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2646275 · Cited by 1 · see citing works
Generative Artificial Intelligence Empowering Higher Education: Current Status and Future Prospects
Xu, G., Li, G. L., & Lu, Y. (2025) · doi:10.1145/3797552.3797659
Nantaburom, S. (2026) · Discover Education · doi:10.1007/s44217-026-01565-z
“This supports newer models of assessment that aim to use AI as a learning partner rather than a shortcut [15, 26].”
Strategic AI-human collaboration for biology assessment design: a practical workflow
Khurana, P., & Kathuria, H. R. (2026) · Journal of Microbiology and Biology Education · doi:10.1128/jmbe.00354-25
Mkhize-Simelane, B., & Khohliso, X. D. (2026) · International Journal of Research in Business and Social Science (2147-4478) · doi:10.20525/ijrbs.v15i2.4970
Toward Intelligent Assessment in HyFlex Learning
Heil, J., Fromm, Y. M., & Ifenthaler, D. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-6182-6.ch010
The AA–EC Framework: Bridging Authentic Assessment and Accreditation in Engineering Education
de Loo, J. V., Chen, Y. L., & Chai, K. K. (2026) · IEEE World Engineering Education Conference (EDUNINE) · doi:10.1109/edunine62390.2026.11547082
AI in foreign language learning and teaching: theory and practice
Liu, S., Wu, R., & Zhang, L. (2026) · Journal of China Computer-Assisted Language Learning · doi:10.1515/jccall-2025-0036
“…educational contexts (Chen et al. 2025), guidelines for integrating AI ethics modules into standard teacher certi fi cation curricula (Jones and Jones 2026), and frameworks for evaluating the pedagogical e ffi cacy of AI tools (Nguyen Thanh et al. 2023) would enhance the book ’ s practical utility.”
Developing AI-Resilient Assessments: A Case Study with Electric Circuits
Barroso, R. J. D., Abril, E. J., Toledo, R. M. L., de Miguel, I., Fernández, P., Aguado, J. C., Bahillo, A., Blas, J., Rosa, R. D. L., Alonso, A., et al. (2026) · IEEE Global Engineering Education Conference (EDUCON) · doi:10.1109/educon67543.2026.11574101
Chaitani, H. (2026) · IntechOpen eBooks · doi:10.5772/intechopen.1016360
Zhang, Z., Fan, S., & Qi, C. (2026) · Behavioral Sciences · doi:10.3390/bs16071227
Elepaño, M. A. D., Manimtim, L., Buendia, B. D., Pasahol, I. S., Abel, J. V., & Sargento, J. U. (2026) · International Conference on e-Education, e-Business, e-Management and e-Learning · doi:10.1109/ic4e69618.2026.11631602
“[20] and [22] noted an opportunity for innovation in Assessment Practices supporting critical thinking and higher-order skills, while acknowledging AI's potential for student cheating.”
InfluentialNon-automatable cognitive skills in higher education in the age of generative AI
Jog, S. (2026) · Frontiers in Education · doi:10.3389/feduc.2026.1827610
The importance of creativity in higher education for the development of modern business
Kovač, D., Terek, E., Ugrinov, S., Ćoćkalo-Hronjec, M., & Cvjetković, M. (2026) · International Symposium Engineering Management and Competitiveness (EMC) · doi:10.5937/emc26138k
Dhinakaran, D., & Raja, S. E. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9089-5.ch005
Junias, K. (2026) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-9089-5.ch008
Evaluación universitaria después de la inteligencia artificial generativa
Rodríguez, J. M. V., Montenegro, C. F. C., Herrera, D. I. B., Pilacuán, D. P. P., & Olaya, O. A. R. (2026) · doi:10.70577/r6870402
- Authors
- Nguyen Thanh, B., Vo, D. T. H., Nguyen Nhat, M., Pham, T. T. T., Thai Trung, H., & Ha Xuan, S.
- Affiliations
- RMIT Vietnam (VN)
- Venue
- Australasian Journal of Educational Technology
- Published
- 2023-12-22
- Volume
- 39
- Issue
- 5
- Pages
- 59-81
- ISSN
- 1449-3098 (ISSN-L)
- 1449-5554
- OpenAlex
- W4390096766
- Cited by
- 87
- References
- 62
- Citations by year
- 2026: 30 · 2025: 33 · 2024: 22 · 2023: 2
- FWCI
- 20.42
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Australasian Journal of Educational Technology (publishedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Education › Educational Assessment and Pedagogy
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- generative AI, authentic assessment, assessment design, Bloom's taxonomy, ChatGPT-4, Google Bard, Bing Chat, economics education, higher-order thinking skills, argumentation, citation and referencing, learning outcomes
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