Commentary Topical Sections

Beyond assessment security: A critical policy analysis of four alternative strategies to uphold academic integrity and adopt the GenAI transformation of teaching and learning for an accredited engineering degree


  • Received: 01 November 2024 Revised: 24 April 2025 Accepted: 19 May 2025 Published: 06 June 2025
  • Generative Artificial Intelligence (GenAI), exemplified by tools such as ChatGPT, has posed significant challenges and opportunities in the realm of academic integrity, particularly in engineering education. This commentary critically examines alternative strategies that go beyond traditional assessment security, aiming to uphold academic integrity while embracing the transformative potential of GenAI in teaching and learning. In the context of programs that rely on unit level outcomes to determine the overall student progression (not the programmatic approach to progression), this study identifies and explores four strategies that may offer potential improvements to assessment security: Ⅰ - risk‑level analysis, which aligns the mix of supervised and unsupervised assessments with their susceptibility to GenAI‑enabled misconduct; Ⅱ - adaptive grade scaling, which links marks for unsupervised work to performance in supervised tasks, thereby discouraging dishonest outsourcing; Ⅲ - gatekeeper units, which embed high‑stakes supervised checkpoints that verify the essential competencies before progression; and Ⅳ - maintaining the status quo, which exposes the limitations of solely relying on the existing security measures. The analysis highlights the potential benefits and weaknesses of each approach, supporting holistic decision-making on policies that improve the ability of students to meet the competency requirements (compared to strategy Ⅳ) while fostering a culture of honesty and ethical behavior. By providing a thorough examination of these strategies, this study contributes valuable insights for educators, policymakers, and researchers, aiming to facilitate a balanced approach that aligns with the accreditation requirements and prepares students for the ethical use of GenAI in their professional careers. This commentary broadens and stimulates discussions on academic integrity in the GenAI era, thus providing practical guidance for educators and institutions.

    Citation: Sasha Nikolic, Montserrat Ros, Yasir M. Al-Abdeli, Helen Fairweather. Beyond assessment security: A critical policy analysis of four alternative strategies to uphold academic integrity and adopt the GenAI transformation of teaching and learning for an accredited engineering degree[J]. STEM Education, 2025, 5(4): 564-586. doi: 10.3934/steme.2025027

    Related Papers:

    [1] Hyo Won Lee, Donald L. DeAngelis, Simeon Yurek, Stephen Tennenbaum . Wading bird foraging on a wetland landscape: a comparison of two strategies. Mathematical Biosciences and Engineering, 2022, 19(8): 7687-7718. doi: 10.3934/mbe.2022361
    [2] Pierre Auger, Tri Nguyen-Huu, Doanh Nguyen-Ngoc . On the impossibility of increasing the MSY in a multisite Schaefer fishing model. Mathematical Biosciences and Engineering, 2025, 22(2): 415-430. doi: 10.3934/mbe.2025016
    [3] Diène Ngom, A. Iggidir, Aboudramane Guiro, Abderrahim Ouahbi . An observer for a nonlinear age-structured model of a harvested fish population. Mathematical Biosciences and Engineering, 2008, 5(2): 337-354. doi: 10.3934/mbe.2008.5.337
    [4] Carlos Camilo-Garay, R. Israel Ortega-Gutiérrez, Hugo Cruz-Suárez . Optimal strategies for a fishery model applied to utility functions. Mathematical Biosciences and Engineering, 2021, 18(1): 518-529. doi: 10.3934/mbe.2021028
    [5] Ali Moussaoui, Pierre Auger, Christophe Lett . Optimal number of sites in multi-site fisheries with fish stock dependent migrations. Mathematical Biosciences and Engineering, 2011, 8(3): 769-783. doi: 10.3934/mbe.2011.8.769
    [6] Huidong Cheng, Hui Xu, Jingli Fu . Dynamic analysis of a phytoplankton-fish model with the impulsive feedback control depending on the fish density and its changing rate. Mathematical Biosciences and Engineering, 2023, 20(5): 8103-8123. doi: 10.3934/mbe.2023352
    [7] Shengyu Huang, Hengguo Yu, Chuanjun Dai, Zengling Ma, Qi Wang, Min Zhao . Dynamics of a harvested cyanobacteria-fish model with modified Holling type Ⅳ functional response. Mathematical Biosciences and Engineering, 2023, 20(7): 12599-12624. doi: 10.3934/mbe.2023561
    [8] Santanu Bhattacharya, Nandadulal Bairagi . Dynamic optimization of fishing tax and tourism fees for sustainable bioeconomic resource management. Mathematical Biosciences and Engineering, 2025, 22(7): 1751-1789. doi: 10.3934/mbe.2025064
    [9] Michele L. Joyner, Chelsea R. Ross, Colton Watts, Thomas C. Jones . A stochastic simulation model for Anelosimus studiosus during prey capture: A case study for determination of optimal spacing. Mathematical Biosciences and Engineering, 2014, 11(6): 1411-1429. doi: 10.3934/mbe.2014.11.1411
    [10] A. Aldurayhim, A. Elsonbaty, A. A. Elsadany . Dynamics of diffusive modified Previte-Hoffman food web model. Mathematical Biosciences and Engineering, 2020, 17(4): 4225-4256. doi: 10.3934/mbe.2020234
  • Generative Artificial Intelligence (GenAI), exemplified by tools such as ChatGPT, has posed significant challenges and opportunities in the realm of academic integrity, particularly in engineering education. This commentary critically examines alternative strategies that go beyond traditional assessment security, aiming to uphold academic integrity while embracing the transformative potential of GenAI in teaching and learning. In the context of programs that rely on unit level outcomes to determine the overall student progression (not the programmatic approach to progression), this study identifies and explores four strategies that may offer potential improvements to assessment security: Ⅰ - risk‑level analysis, which aligns the mix of supervised and unsupervised assessments with their susceptibility to GenAI‑enabled misconduct; Ⅱ - adaptive grade scaling, which links marks for unsupervised work to performance in supervised tasks, thereby discouraging dishonest outsourcing; Ⅲ - gatekeeper units, which embed high‑stakes supervised checkpoints that verify the essential competencies before progression; and Ⅳ - maintaining the status quo, which exposes the limitations of solely relying on the existing security measures. The analysis highlights the potential benefits and weaknesses of each approach, supporting holistic decision-making on policies that improve the ability of students to meet the competency requirements (compared to strategy Ⅳ) while fostering a culture of honesty and ethical behavior. By providing a thorough examination of these strategies, this study contributes valuable insights for educators, policymakers, and researchers, aiming to facilitate a balanced approach that aligns with the accreditation requirements and prepares students for the ethical use of GenAI in their professional careers. This commentary broadens and stimulates discussions on academic integrity in the GenAI era, thus providing practical guidance for educators and institutions.





    [1] Mollick, E., Co-intelligence: living and working with AI. 2024, London, UK: WH Allen.
    [2] Dawson, P., Five ways to hack and cheat with bring-your-own-device electronic examinations. British Journal of Educational Technology, 2016, 47(4): 592‒600. https://doi.org/10.1111/bjet.12246 doi: 10.1111/bjet.12246
    [3] Ellis, C. and Murdoch, K., The educational integrity enforcement pyramid: a new framework for challenging and responding to student cheating. Assessment & Evaluation in Higher Education, 2024, 49(7): 924‒934. https://doi.org/10.1080/02602938.2024.2329167 doi: 10.1080/02602938.2024.2329167
    [4] Cotton, D.R.E., Cotton, P.A. and Shipway, J.R., Chatting and cheating: ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 2023, 61(2): 228‒239. https://doi.org/10.1080/14703297.2023.2190148 doi: 10.1080/14703297.2023.2190148
    [5] Palmer, K., Sting operation fools a proctoring service—and results in blackmail attempt, in Inside Higher Ed. 2024. Available from: https://www.insidehighered.com/news/students/academics/2024/03/28/sting-operation-fools-proctoring-service-blackmail-attempted
    [6] Nikolic, S., Daniel, S., Haque, R., Belkina, M., Hassan, G.M., Grundy, S., et al., ChatGPT versus engineering education assessment: a multidisciplinary and multi-institutional benchmarking and analysis of this generative artificial intelligence tool to investigate assessment integrity. European Journal of Engineering Education, 2023, 48(4): 559‒614. https://doi.org/10.1080/03043797.2023.2213169 doi: 10.1080/03043797.2023.2213169
    [7] Nikolic, S., Sandison, C., Haque, R., Daniel, S., Grundy, S., Belkina, M., et al., 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. Australasian Journal of Engineering Education, 2024, 29(2): 126‒153. https://doi.org/10.1080/22054952.2024.2372154 doi: 10.1080/22054952.2024.2372154
    [8] Lodge, J.M., de Barba, P. and Broadbent, J., Learning with generative artificial intelligence within a network of co-regulation. Journal of University Teaching and Learning Practice, 2023, 20(7): 1‒10. https://doi.org/10.53761/1.20.7.02 doi: 10.53761/1.20.7.02
    [9] Abeysekera, I., ChatGPT and academia on accounting assessments. Journal of Open Innovation: Technology, Market, and Complexity, 2024, 10(1): 100213. https://www.sciencedirect.com/science/article/pii/S2199853124000076.
    [10] Mustapha, K.B., Yap, E.H. and Abdalla Abakr, Y., Bard, ChatGPT and 3DGPT: a scientometric analysis of generative AI tools and assessment of implications for mechanical engineering education. Interactive Technology and Smart Education, 2024. https://doi.org/10.1108/ITSE-10-2023-0198 doi: 10.1108/ITSE-10-2023-0198
    [11] McDonald, N., Johri, A., Ali, A. and Collier, A.H., Generative artificial intelligence in higher education: evidence from an analysis of institutional policies and guidelines. Computers in Human Behavior: Artificial Humans, 2025, 3: 100121. https://doi.org/10.1016/j.chbah.2025.100121 doi: 10.1016/j.chbah.2025.100121
    [12] Luo, J., A critical review of GenAI policies in higher education assessment: a call to reconsider the "originality" of students' work. Assessment & Evaluation in Higher Education, 2024, 49(5): 651‒664. https://doi.org/10.1080/02602938.2024.2309963 doi: 10.1080/02602938.2024.2309963
    [13] An, Y., Yu, J.H. and James, S., Investigating the higher education institutions guidelines and policies regarding the use of generative AI in teaching, learning, research, and administration. International Journal of Educational Technology in Higher Education, 2025, 22(1). https://doi.org/10.1186/s41239-025-00507-3 doi: 10.1186/s41239-025-00507-3
    [14] Dai, Y., Lai, S., Lim, C.P. and Liu, A., University policies on generative AI in Asia: promising practices, gaps, and future directions. Journal of Asian Public Policy, 2025: 1‒22. https://doi.org/10.1080/17516234.2024.2379070 doi: 10.1080/17516234.2024.2379070
    [15] Yu, H., Reflection on whether ChatGPT should be banned by academia from the perspective of education and teaching. Frontiers in Psychology, 2023, 14: 1181712. https://doi.org/10.3389/fpsyg.2023.1181712 doi: 10.3389/fpsyg.2023.1181712
    [16] Jiang, Y., Xie, L., Lin, G. and Mo, F., Widen the debate: what is the academic community's perception on ChatGPT? Education and Information Technologies, 2024, 29(1): 20181‒20200. https://doi.org/10.1007/s10639-024-12677-0 doi: 10.1007/s10639-024-12677-0
    [17] Ansari, A.N., Ahmad, S. and Bhutta, S.M., Mapping the global evidence around the use of ChatGPT in higher education: a systematic scoping review. Education and Information Technologies, 2024, 29(1): 11281‒11321. https://doi.org/10.1007/s10639-023-12223-4 doi: 10.1007/s10639-023-12223-4
    [18] Cong-Lem, N., Soyoof, A. and Tsering, D., A systematic review of the limitations and associated opportunities of ChatGPT. International Journal of Human–Computer Interaction, 2025, 41(7): 3851‒3866. https://doi.org/10.1080/10447318.2024.2344142 doi: 10.1080/10447318.2024.2344142
    [19] Quince, Z. and Nikolic, S., Student identification of the social, economic and environmental implications of using generative artificial intelligence (GenAI): identifying student ethical awareness of ChatGPT from a scaffolded multi-stage assessment. European Journal of Engineering Education, 2025, 1‒20. https://doi.org/10.1080/03043797.2025.2482830 doi: 10.1080/03043797.2025.2482830
    [20] Liu, Q., Hu, A., Gladman, T. and Gallagher, S., Eight months into reality: a scoping review of the application of ChatGPT in higher education teaching and learning. Innovative Higher Education, 2025, 1‒24. https://doi.org/10.1007/s10755-025-09790-4 doi: 10.1007/s10755-025-09790-4
    [21] Nikolic, S., Wentworth, I., Sheridan, L., Moss, S., Duursma, E., Jones, R.A., et al., A systematic literature review of attitudes, intentions and behaviours of teaching academics pertaining to AI and generative AI (GenAI) in higher education: an analysis of GenAI adoption using the UTAUT framework. Australasian Journal of Educational Technology, 2024, 40(6): 56‒75. https://doi.org/10.14742/ajet.9643 doi: 10.14742/ajet.9643
    [22] Neupane, A., Shahi, T., Cowling, M. and Tanna, D., Threading the GenAI needle: unpacking the ups and downs of GenAI for higher education stakeholders. Journal of Applied Learning and Teaching, 2024, 7(2): 1‒9. https://doi.org/10.37074/jalt.2024.7.2 doi: 10.37074/jalt.2024.7.2
    [23] Bell, M., The impact of AI and generative technologies on the engineering profession. Engineers Australia, 2025. Available from: https://www.engineersaustralia.org.au/sites/default/files/2025-01/impact-ai-generative-technologies-engineering-profession_0.pdf
    [24] Australian Qualifications Framework. 2016, Accessed 12 Feb 2016. http://www.aqf.edu.au/
    [25] Engineers Australia, G02 Accreditation Criteria Guidelines. 2008, Accessed 25 July 2017. Weblink no longer active.
    [26] Engineers Australia, Stage 1 competency standard for professional engineers. 2019, retrieved 07/02/25. https://www.engineersaustralia.org.au/accreditation-management-system
    [27] Daniel, S., et al., Engineering assessment in the age of generative artificial intelligence: a critical analysis, in 2024 World Engineering Education Forum – Global Engineering Deans Council (WEEF-GEDC), Sydney, Australia, 2024.
    [28] Dawson, P., Validity matters more than cheating. Assessment & Evaluation in Higher Education, 2024, 49(7): 1005‒1016. https://doi.org/10.1080/02602938.2024.2386662 doi: 10.1080/02602938.2024.2386662
    [29] Lodge, J., Howard, S. and Bearman, M., Assessment reform for the age of artificial intelligence. Tertiary Education Quality and Standards Agency, 2023. https://www.teqsa.gov.au/guides-resources/resources/corporate-publications/assessment-reform-age-artificial-intelligence
    [30] van der Vleuten, C.P.M., Schuwirth, L.W.T., Driessen, E.W., Dijkstra, J., Tigelaar, D., Baartman, L.K.J. and Van Tartwijk, J., A model for programmatic assessment fit for purpose. Medical Teacher, 2012, 34(3): 205‒214. https://doi.org/10.3109/0142159X.2012.652239 doi: 10.3109/0142159X.2012.652239
    [31] Ali, A., Collier, A.H., Dewan, U., McDonald, N. and Johri, A., Analysis of generative AI policies in computing course syllabi, in Proceedings of the 56th ACM Technical Symposium on Computer Science Education. 2025.
    [32] Corbin, T., Dawson, P. and Liu, D., Talk is cheap: why structural assessment changes are needed for a time of GenAI. Assessment & Evaluation in Higher Education, 2025, 1‒11. https://doi.org/10.1080/02602938.2025.2503964 doi: 10.1080/02602938.2025.2503964
    [33] Tlili, A., Shehata, B., Adarkwah, M.A., Bozkurt, A., Hickey, D.T., Huang, R. and Agyemang, B., What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 2023, 10(1): 15. https://doi.org/10.1186/s40561-023-00237-x doi: 10.1186/s40561-023-00237-x
    [34] Elkhodr, M., Gide, E., Wu, R. and Darwish, O., ICT students' perceptions towards ChatGPT: an experimental reflective lab analysis. STEM Education, 2023, 3(2): 70‒88. https://doi.org/10.3934/steme.2023006 doi: 10.3934/steme.2023006
    [35] van Dis, E.A.M., Bollen, J., Zuidema, W., van Rooij, R. and Bockting, C.L., ChatGPT: five priorities for research. Nature, 2023,614(7947): 224‒226.
    [36] Purtill, J., ChatGPT was tipped to cause widespread cheating. Here's what students say happened, in ABC Australia. 2023. https://www.abc.net.au/news/science/2023-11-22/how-high-school-students-used-chatgpt-2023-education-cheating/103108620
    [37] Belkina, M., Daniel, S., Nikolic, S., Haque, R., Lyden, S., Neal, P., et al., Implementing generative AI (GenAI) in higher education: a systematic review of case studies. Computers and Education: Artificial Intelligence, 2025, 100407. https://doi.org/10.1016/j.caeai.2025.100407 doi: 10.1016/j.caeai.2025.100407
    [38] Fatahi, B., Nguyen, L.D., Khabbaz, H. and Hadgraft, R., Virtual teammates: transforming engineering learning through generative AI integration. In 35th Australasian Association of Engineering Education Conference, Christchurch, New Zealand, 2024.
    [39] Ruiz-Rojas, L.I., Acosta-Vargas, P., De-Moreta-Llovet, J. and Gonzalez-Rodriguez, M., Empowering education with generative artificial intelligence tools: approach with an instructional design matrix. Sustainability, 2023, 15(15): 11524. https://doi.org/10.3390/su151511524 doi: 10.3390/su151511524
    [40] Barrett, A. and Pack, A., Not quite eye to AI: student and teacher perspectives on the use of generative artificial intelligence in the writing process. International Journal of Educational Technology in Higher Education, 2023, 20(1): 1‒24. https://doi.org/10.1186/s41239-023-00427-0 doi: 10.1186/s41239-023-00427-0
    [41] Nikolic, S., Quince, Z., Lidfors Lindqvist, A., Neal, P., Grundy, S., Lim, M., et al., Project-work artificial intelligence integration framework (PAⅡF): developing a CDIO-based framework for educational integration. STEM Education, 2025, 5(2): 310‒332. https://doi.org/10.3934/steme.2025016 doi: 10.3934/steme.2025016
    [42] Pinzolits, R., AI in academia: an overview of selected tools and their areas of application. MAP Education and Humanities, 2024, 4: 37‒50. https://doi.org/10.53880/2744-2373.2023.4.37 doi: 10.53880/2744-2373.2023.4.37
    [43] Bearman, M., Nieminen, J.H. and Ajjawi, R., Designing assessment in a digital world: an organising framework. Assessment & Evaluation in Higher Education, 2023, 48(3): 291‒304. https://doi.org/10.1080/02602938.2022.2069674 doi: 10.1080/02602938.2022.2069674
    [44] Bearman, M., Ajjawi, R., Boud, D., Tai, J. and Dawson, P., Cradle suggests… assessment and GenAI. Centre for Research in Assessment and Digital Learning, Deakin University, Melbourne, Australia, 2023. https://doi.org/10.6084/m9.figshare.22494178
    [45] Belkina, M., Neal, P., Grundy, S., Hassan, G.M., Haque, R., Daniel, S., et al., Systematic literature review of GenAI integration in higher education and analysis of opportunities for engineering education. In Proceedings of the 35th Annual Conference of the Australasian Association for Engineering Education (AAEE 2024), Christchurch, New Zealand. 2024, 1‒10.
    [46] Dawson, P., Defending assessment security in a digital world: preventing e-cheating and supporting academic integrity in higher education, 2020, New York, USA: Routledge.
    [47] Noorbehbahani, F., Mohammadi, A. and Aminazadeh, M., A systematic review of research on cheating in online exams from 2010 to 2021. Education and Information Technologies, 2022, 27(6): 8413‒8460. https://doi.org/10.1007/s10639-022-10927-7 doi: 10.1007/s10639-022-10927-7
    [48] Burgess, B., Ginsberg, A., Felten, E.W. and Cohney, S., Watching the watchers: bias and vulnerability in remote proctoring software. In 31st USENIX Security Symposium (USENIX Security 22), Boston, MA, USA, 2022.
    [49] Bergmans, L., Bouali, N., Luttikhuis, M. and Rensink, A., On the efficacy of online proctoring using Proctorio. In 13th International Conference on Computer Supported Education (CSEDU 2021), 2021.
    [50] Dawson, P., Remote proctoring: understanding the debate, in Second Handbook of Academic Integrity, 2024, 1511‒1526. Springer.
    [51] University of Technology Sydney, Next steps for GenAI and assessment reform at UTS: a response to TEQSA. 2024. https://educationexpress.uts.edu.au/blog/2024/09/02/next-steps-for-genai-and-assessment-reform-uts-response-teqsa/
    [52] Hurdle requirements. 2025, accessed 19/05/2025. https://www.adelaide.edu.au/learning/resources-for-educators/assessment/hurdle-requirements
    [53] GenAI and integrity: the arguments to reshape the thesis assessment structure. 2024, accessed 08/10/2025. https://www.aaieec.org/post/genai-and-integrity-the-arguments-to-reshape-the-thesis-assessment-structure.
    [54] Nikolic, S., Suesse, T.F., Grundy, S., Haque, R., Lyden, S., Hassan, G.M., et al., Laboratory learning objectives: ranking objectives across the cognitive, psychomotor and affective domains within engineering. European Journal of Engineering Education, 2024, 49(4): 559‒614. https://doi.org/10.1080/03043797.2023.2248042 doi: 10.1080/03043797.2023.2248042
    [55] Seery, M.K., Agustian, H.Y., Doidge, E.D., Kucharski, M.M., O'Connor, H.M. and Price, A., Developing laboratory skills by incorporating peer-review and digital badges. Chemistry Education Research and Practice, 2017, 18(3): 403‒419. https://doi.org/10.1039/C7RP00003K doi: 10.1039/C7RP00003K
    [56] Dunne, I. and Nikolic, S., Autonomous assessment of a laboratory exam for the digital hardware curriculum. In 2021 IEEE International Conference on Engineering, Technology & Education (TALE), Wuhan, China, 2021.
    [57] Nikolic, S., Suesse, T.F., Grundy, S., Haque, R., Lyden, S., Lal, S., et al., Assessment integrity and validity in the teaching laboratory: adapting to GenAI by developing an understanding of the verifiable learning objectives behind laboratory assessment selection. European Journal of Engineering Education, 2025, 1‒28. https://doi.org/10.1080/03043797.2025.2456944 doi: 10.1080/03043797.2025.2456944
    [58] Bearman, M., Tai, J., Dawson, P., Boud, D. and Ajjawi, R., Developing evaluative judgement for a time of generative artificial intelligence. Assessment & Evaluation in Higher Education, 2024, 49(6): 893‒905. https://doi.org/10.1080/02602938.2024.2335321 doi: 10.1080/02602938.2024.2335321
    [59] Frequently asked questions about the two-lane approach to assessment in the age of AI. 2024, accessed 31/07/2024. https://educational-innovation.sydney.edu.au/teaching@sydney/frequently-asked-questions-about-the-two-lane-approach-to-assessment-in-the-age-of-ai/
    [60] Yale-Soulière, G., Campeau, G., Turgeon, L. and Goulet, J., Evaluation of a brief intervention to reduce test anxiety in adolescents: a randomized control trial. Current Psychology, 2024, 43(39): 30760‒30775. https://doi.org/10.1007/s12144-024-06361-2 doi: 10.1007/s12144-024-06361-2
    [61] Smith, A., McConnell, L., Iyer, P., Allman-Farinelli, M. and Chen, J., Co-designing assessment tasks with students in tertiary education: a scoping review of the literature. Assessment & Evaluation in Higher Education, 2025, 50(2): 199‒218. https://doi.org/10.1080/02602938.2024.2376648 doi: 10.1080/02602938.2024.2376648
    [62] Leenknecht, M., Wijnia, L., Köhlen, M., Fryer, L., Rikers, R. and Loyens, S., Formative assessment as practice: the role of students' motivation. Assessment & Evaluation in Higher Education, 2021, 46(2): 236‒255. https://doi.org/10.1080/02602938.2020.1765228 doi: 10.1080/02602938.2020.1765228
    [63] Ferrara, E., GenAI against humanity: nefarious applications of generative artificial intelligence and large language models. Journal of Computational Social Science, 2024, 7: 549‒569. https://doi.org/10.1007/s42001-024-00250-1 doi: 10.1007/s42001-024-00250-1
    [64] Herbold, S., Hautli-Janisz, A., Heuer, U., Kikteva, Z. and Trautsch, A., A large-scale comparison of human-written versus ChatGPT-generated essays. Scientific Reports, 2023, 13(1): 18617. https://doi.org/10.1038/s41598-023-45644-9 doi: 10.1038/s41598-023-45644-9
  • This article has been cited by:

    1. A. Obaza, D. L. DeAngelis, J. C. Trexler, Using data from an encounter sampler to model fish dispersal, 2011, 78, 00221112, 495, 10.1111/j.1095-8649.2010.02867.x
    2. Maria C. Loinaz, Dayna Gross, Robert Unnasch, Michael Butts, Peter Bauer-Gottwein, Modeling ecohydrological impacts of land management and water use in the Silver Creek basin, Idaho, 2014, 119, 21698953, 487, 10.1002/2012JG002133
  • Author's biography Sasha Nikolic is a Senior Lecturer at the University of Wollongong with a PhD in Engineering Education. He has worked in academia and industry. Dr. Nikolic has received multiple teaching and learning awards, including Australian Awards for University Teaching in 2012 and 2019, and AAEE awards in 2019 and 2023. He served in multiple governance roles with IEEE, and is an Associate Editor of the European Journal of Engineering Education. He currently serves as President for both the Australasian Association of Engineering Education and the Australasian Artificial Intelligence in Engineering Education Centre, where he leads multi-institutional initiatives on Generative AI. Dr; Montserrat Ros is an Associate Professor in the School of Electrical, Computer & Telecommunications Engineering and Associate Dean Education in the Faculty of Engineering and Information Sciences at the University of Wollongong, Australia. Montse graduated from a BE(Comp. Sys)/BSc(Math) with First Class Honours in 2000 and obtained her Ph.D. in Computer Engineering in 2007; both from the University of Queensland, Australia. Her research interests include Embedded Systems, Internet of Things, Sensor Networks Data Fusion, Code Compression and Engineering Education and has published over 100 peer-reviewed papers. Montse was awarded a 2019 Citation for Outstanding Contributions to Student Learning at the Australian Awards for University Teaching; and was named a UOW 2016 Woman of Impact for inspiring the young STEM generation. Dr; Yasir M. Al-Abdeli (Ph.D. USyd 2004) is an Associate Dean Teaching and Learning in a School of Engineering. Whilst his main areas of applied research are Thermofluids and energy, he has published on a number of topics within engineering education, including flipped classes and problem-based learning. He has a passion for continuous change in higher education enabled by technological change coupled, sound curriculum design, and good pedagogy so as to support learners in their lifelong learning. He is the co-founder of a community of practice on blended learning in Perth, Western Australia. Dr; Helen Fairweather is Head of Accreditation at Engineers Australia. She has over a decade of experience as an environmental engineering academic at the University of the Sunshine Coast, where she integrated professional and academic experience into the engineering curriculum during a period of significant societal change. Dr Fairweather now applies this expertise to influence the engineering profession at a national level. Her recent work has focused on promoting diversity in engineering education and highlighting the critical role of engineers in advancing the United Nations Sustainable Development Goals
    Reader Comments
  • © 2025 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Metrics

Article views(522) PDF downloads(43) Cited by(0)

Article outline

Figures and Tables

Tables(2)

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog