Is GenAI the Future of Feedback? Understanding Student and Staff Perspectives on AI in Assessment

Authors

DOI:

https://doi.org/10.70770/rzzz6y35

Keywords:

Assessment, GenAI, Generative Artificial Intelligence (GenAI), Education, AI in education, Feedback

Abstract

The rise of Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI) in higher education necessitates assessment reform. This study addresses a critical gap by exploring student and academic staff experiences with AI and GenAI tools, focusing on their familiarity and comfort with current and potential future applications in learning and assessment. An online survey collected data from 35 academic staff and 282 students across two universities in Vietnam and one in Singapore, examining GenAI familiarity, perceptions of its use in assessment marking and feedback, knowledge checking and participation, and experiences of GenAI text detection. Descriptive statistics and reflexive thematic analysis revealed a generally low familiarity with GenAI among both groups. GenAI feedback was viewed negatively; however, it was viewed more positively when combined with instructor feedback. Academic staff were more accepting of GenAI text detection tools and grade adjustments based on detection results compared to students. Qualitative analysis identified three themes: unclear understanding of text detection tools, variability in experiences with GenAI detectors, and mixed feelings about GenAI’s future impact on educational assessment. These findings have major implications regarding the development of policies and practices for GenAI-enabled assessment and feedback in higher education.

Downloads

Download data is not yet available.

References

Abdaljaleel, M., Barakat, M., Alsanafi, M., Salim, N. A., Abazid, H., Malaeb, D., Mohammed, A. H., Hassan, B. A. R., Wayyes, A. M., Farhan, S. S., Khatib, S. E., Rahal, M., Sahban, A., Abdelaziz, D. H., Mansour, N. O., AlZayer, R., Khalil, R., Fekih-Romdhane, F., Hallit, R., … Sallam, M. (2024). A multinational study on the factors influencing university students’ attitudes and usage of ChatGPT. Scientific Reports, 14(1), 1983. https://doi.org/10.1038/s41598-024-52549-8

Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning (SSRN Scholarly Paper 4337484). https://doi.org/10.2139/ssrn.4337484

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, 0(0), 1–13. https://doi.org/10.1080/02602938.2024.2335321

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Braun, V., & Clarke, V. (2019). Reflecting on reflexive thematic analysis. Qualitative Research in Sport, Exercise and Health, 11(4), 589–597. https://doi.org/10.1080/2159676X.2019.1628806

Braun, V., Clarke, V., & Hayfield, N. (2022). ‘A starting point for your journey, not a map’: Nikki Hayfield in conversation with Virginia Braun and Victoria Clarke about thematic analysis. Qualitative Research in Psychology, 19(2), 424–445. https://doi.org/10.1080/14780887.2019.1670765

Chaka, C. (2024). Accuracy pecking order – How 30 AI detectors stack up in detecting generative artificial intelligence content in university English L1 and English L2 student essays. Journal of Applied Learning and Teaching, 7(1), Article 1. https://doi.org/10.37074/jalt.2024.7.1.33

Chan, C. K. Y. (2023). A comprehensive AI policy education framework for university teaching and learning. International Journal of Educational Technology in Higher Education, 20(1), 38. https://doi.org/10.1186/s41239-023-00408-3

Chan, C. K. Y., & Lee, K. K. W. (2023). The AI generation gap: Are Gen Z students more interested in adopting generative AI such as ChatGPT in teaching and learning than their Gen X and millennial generation teachers? Smart Learning Environments, 10(1), 60. https://doi.org/10.1186/s40561-023-00269-3

Chan, C. K. Y., & Zhou, W. (2023). An expectancy value theory (EVT) based instrument for measuring student perceptions of generative AI. Smart Learning Environments, 10(1), 64. https://doi.org/10.1186/s40561-023-00284-4

Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2023). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International, 0(0), 1–12. https://doi.org/10.1080/14703297.2023.2190148

Crawford, J., Cowling, M., Ashton-Hay, S., Kelder, J.-A., Middleton, R., & Wilson, G. (2023). Artificial Intelligence and Authorship Editor Policy: ChatGPT, Bard Bing AI, and beyond. Journal of University Teaching & Learning Practice, 20(5). https://doi.org/10.53761/1.20.5.01

Dai, W., Lin, J., Jin, F., Li, T., Tsai, Y.-S., Gasevic, D., & Chen, G. (2023). Can Large Language Models Provide Feedback to Students? A Case Study on ChatGPT. EdArXiv. https://doi.org/10.35542/osf.io/hcgzj

Firat, M. (2023). What ChatGPT means for universities: Perceptions of scholars and students. Journal of Applied Learning and Teaching, 6(1), 57–63.

Furze, L., Perkins, M., Roe, J., & MacVaugh, J. (2024). The AI Assessment Scale (AIAS) in action: A pilot implementation of GenAI supported assessment (arXiv:2403.14692). arXiv. https://doi.org/10.48550/arXiv.2403.14692

Ghimire, A., Prather, J., & Edwards, J. (2024). Generative AI in Education: A Study of Educators’ Awareness, Sentiments, and Influencing Factors (arXiv:2403.15586). arXiv. http://arxiv.org/abs/2403.15586

Ifelebuegu, A. (2023). Rethinking online assessment strategies: Authenticity versus AI chatbot intervention. Journal of Applied Learning and Teaching, 6(2).

Kim, J., Merrill, K., Xu, K., & Sellnow, D. D. (2020). My Teacher Is a Machine: Understanding Students’ Perceptions of AI Teaching Assistants in Online Education. International Journal of Human–Computer Interaction, 36(20), 1902–1911. https://doi.org/10.1080/10447318.2020.1801227

Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., Lekkas, D., & Palmer, E. (2024). The impact of generative AI on higher education learning and teaching: A study of educators’ perspectives. Computers and Education: Artificial Intelligence, 6, 100221. https://doi.org/10.1016/j.caeai.2024.100221

Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. arXiv Preprint arXiv:2304.02819.

Luo (Jess), J. (2024). How does GenAI affect trust in teacher-student relationships? Insights from students’ assessment experiences. Teaching in Higher Education, 0(0), 1–16. https://doi.org/10.1080/13562517.2024.2341005

Messeri, L., & Crockett, M. J. (2024). Artificial intelligence and illusions of understanding in scientific research. Nature, 627(8002), 49–58. https://doi.org/10.1038/s41586-024-07146-0

Mizumoto, A., & Eguchi, M. (2023). Exploring the potential of using an AI language model for automated essay scoring. Research Methods in Applied Linguistics, 2(2), 100050. https://doi.org/10.1016/j.rmal.2023.100050

Newton, P., & Xiromeriti, M. (2024). ChatGPT performance on multiple choice question examinations in higher education. A pragmatic scoping review. Assessment & Evaluation in Higher Education, 0(0), 1–18. https://doi.org/10.1080/02602938.2023.2299059

Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w

Nikolic, S., Daniel, S., Haque, R., Belkina, M., Hassan, G. M., Grundy, S., Lyden, S., Neal, P., & Sandison, C. (2023). 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, 48(4), 559–614. https://doi.org/10.1080/03043797.2023.2213169

Perkins, M. (2023). Academic Integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice, 20(2). https://doi.org/10.53761/1.20.02.07

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(06), Article 06. https://doi.org/10.53761/q3azde36

Perkins, M., & Roe, J. (2023a). Academic publisher guidelines on AI usage: A ChatGPT supported thematic analysis. F1000Research, 12. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10844801/

Perkins, M., & Roe, J. (2023b). Decoding Academic Integrity Policies: A Corpus Linguistics Investigation of AI and Other Technological Threats. Higher Education Policy. https://doi.org/10.1057/s41307-023-00323-2

Perkins, M., Roe, J., Postma, D., McGaughran, J., & Hickerson, D. (2023). Detection of GPT-4 Generated Text in Higher Education: Combining Academic Judgement and Software to Identify Generative AI Tool Misuse. Journal of Academic Ethics. https://doi.org/10.1007/s10805-023-09492-6

Perkins, M., Roe, J., Vu, B. H., Postma, D., Hickerson, D., McGaughran, J., & Khuat, H. Q. (2024). Simple techniques to bypass GenAI text detectors: Implications for inclusive education. International Journal of Educational Technology in Higher Education, 21, Article 53. https://doi.org/10.1186/s41239-024-00487-w

Polyportis, A., & Pahos, N. (2024). Navigating the perils of artificial intelligence: A focused review on ChatGPT and responsible research and innovation. Humanities and Social Sciences Communications, 11(1), 1–10. https://doi.org/10.1057/s41599-023-02464-6

Ramesh, D., & Sanampudi, S. K. (2022). An automated essay scoring systems: A systematic literature review. Artificial Intelligence Review, 55(3), 2495–2527. https://doi.org/10.1007/s10462-021-10068-2

Roe, J. (2022). Reconceptualizing academic dishonesty as a struggle for intersubjective recognition: A new theoretical model. Humanities and Social Sciences Communications, 9(1).

Roe, J., Renandya, W. A., & Jacobs, G. M. (2023). A review of AI-powered writing tools and their implications for academic integrity in the language classroom. Journal of English and Applied Linguistics, 2(1), 3.

Roe, J., & Perkins, M. (2022). What are Automated Paraphrasing Tools and how do we address them? A review of a growing threat to academic integrity. International Journal for Educational Integrity, 18(1), 15.

Roe, J., Perkins, M., Chonu, G. K., & Bhati, A. (2024). Student perceptions of peer cheating behaviour during COVID-19 induced online teaching and assessment. Higher Education Research & Development, 43(4), 966-980.

Roszkowski, M. J., & Soven, M. (2010). Shifting gears: Consequences of including two negatively worded items in the middle of a positively worded questionnaire. Assessment & Evaluation in Higher Education, 35(1), 113–130. https://doi.org/10.1080/02602930802618344

Rudolph, J., Tan, S., & Tan, S. (2023a). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?. Journal of applied learning and teaching, 6(1), 342-363.

Rudolph, J., Tan, S., & Tan, S. (2023b). War of the chatbots: Bard, Bing Chat, ChatGPT, Ernie and beyond. The new AI gold rush and its impact on higher education. Journal of Applied Learning and Teaching, 6(1), 364-389.

Scotland, J. (2016). How the experience of assessed collaborative writing impacts on undergraduate students’ perceptions of assessed group work. Assessment & Evaluation in Higher Education, 41(1), 15–34. https://doi.org/10.1080/02602938.2014.977221

Sevnarayan, K., & Potter, M.-A. (2024). Generative Artificial Intelligence in distance education: Transformations, challenges, and impact on academic integrity and student voice. Journal of Applied Learning and Teaching, 7(1), Article 1. https://doi.org/10.37074/jalt.2024.7.1.41

Smolansky, A., Cram, A., Raduescu, C., Zeivots, S., Huber, E., & Kizilcec, R. F. (2023). Educator and Student Perspectives on the Impact of Generative AI on Assessments in Higher Education. Proceedings of the Tenth ACM Conference on Learning @ Scale, 378–382. https://doi.org/10.1145/3573051.3596191

Spooren, P., Mortelmans, D., & Denekens, J. (2007). Student evaluation of teaching quality in higher education: Development of an instrument based on 10 Likert‐scales. Assessment & Evaluation in Higher Education, 32(6), 667–679. https://doi.org/10.1080/02602930601117191

Stančić, M. (2021). Peer assessment as a learning and self-assessment tool: A look inside the black box. Assessment & Evaluation in Higher Education, 46(6), 852–864. https://doi.org/10.1080/02602938.2020.1828267

Swiecki, Z., Khosravi, H., Chen, G., Martinez-Maldonado, R., Lodge, J. M., Milligan, S., Selwyn, N., & Gašević, D. (2022). Assessment in the age of artificial intelligence. Computers and Education: Artificial Intelligence, 3, 100075. https://doi.org/10.1016/j.caeai.2022.100075

Wang, X., Pang, H., Wallace, M. P., Wang, Q., & Chen, W. (2022). Learners’ perceived AI presences in AI-supported language learning: A study of AI as a humanized agent from community of inquiry. Computer Assisted Language Learning, 1–27.

Weber-Wulff, D., Anohina-Naumeca, A., Bjelobaba, S., Foltýnek, T., Guerrero-Dib, J., Popoola, O., Šigut, P., & Waddington, L. (2023). Testing of detection tools for AI-generated text. International Journal for Educational Integrity, 19(1), Article 1. https://doi.org/10.1007/s40979-023-00146-z

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(1), 40. https://doi.org/10.1186/s41239-024-00468-z

Downloads

Published

2024-11-01

Issue

Section

Articles