AI-Supported Formative Assessments: Enhancing Student-Centered Learning and Teacher Perceptions

Mojahed Mousa

doi:10.56741/jpes.v4i02.857

Authors

Mojahed Mousa
mojahed.mousa@staff.alquds.edu
Al-Quds University

Keywords:

Academic Integrity, Artificial Intelligent, Ethical Consideration, Formative Assessments, Student-Centered Learning

Abstract

This study evaluates the effectiveness of AI-supported formative assessments in student-centered learning. Using a reliable questionnaire with a Cronbach's alpha of .854, the research explores teachers' views on AI tools and their impact on formative assessment practices. The findings reveal a generally positive attitude among educators, with an average score of 92.79 and a moderate standard deviation of 9.03, indicating a strong appreciation for AI's role in data collection, analysis, and the creation of innovative assessment tasks. However, the variability in responses points to differing levels of familiarity and perception regarding AI. Interestingly, there were no significant differences in opinions among teachers from public, private, or UNRWA schools, suggesting that factors beyond the type of school play a role in shaping perceptions of AI in formative assessment. Ethical concerns also emerged, including issues related to data privacy, intellectual property, and the potential effects on critical thinking skills. These concerns underscore the necessity for robust data protection policies, ethical guidelines, and comprehensive educational training to mitigate risks and foster responsible AI use. The study concludes that while AI has the potential to greatly enhance formative assessments by boosting efficiency and offering valuable insights, its integration must be approached with careful attention to ethical considerations and academic integrity. Establishing clear guidelines and training programs is crucial for effectively utilizing AI in a student-centered learning environment.

Downloads

Download data is not yet available.

Author Biography

Mojahed Mousa, Al-Quds University

is a dedicated faculty member at Al-Quds Bard College, affiliated with Al-Quds University in Palestine. He is actively involved in the Master of Arts in Teaching (MAT) program, where he teaches courses that integrate pedagogical theory with practical applications in classroom settings. His academic background and professional expertise are rooted in advancing effective teaching methodologies, with a particular focus on educational technology and assessment practices. His research interests include the integration of technology in teaching and learning, formative and summative assessment, and teacher professional development. (email: mojahed.mousa@staff.alquds.edu).

References

González-Calatayud, V., Prendes-Espinosa, P., & Roig-Vila, R. (2021). Artificial intelligence for student assessment: A systematic review. Applied Sciences, 11(12), 5467. https://doi.org/10.3390/app11125467 DOI: https://doi.org/10.3390/app11125467

Shute, V. J., & Rahimi, S. (2017). Review of computer-based assessment for learning in elementary and secondary education. Journal of Computer Assisted Learning, 33(1), 1–19. https://doi.org/10.1111/jcal.12172 DOI: https://doi.org/10.1111/jcal.12172

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed: An argument for AI in education. Pearson Education.

Woolf, B. P. (2007). Building Intelligent Interactive Tutors: Student-centered strategies for revolutionizing e-learning. Elsevier & Morgan Kaufmann

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Center for Curriculum Redesign.

Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014). Intelligent tutoring systems and learning outcomes: A meta-analysis. Journal of Educational Psychology, 106(4), 901–918. https://doi.org/10.1037/a0037123 DOI: https://doi.org/10.1037/a0037123

VanLehn, K. (2011). The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist, 46(4), 197-221. http://dx.doi.org/10.1080/00461520.2011.611369 DOI: https://doi.org/10.1080/00461520.2011.611369

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0174-0 DOI: https://doi.org/10.1186/s41239-019-0171-0

Black, P., & Wiliam, D. (2009). Developing the theory of formative assessment. Educational Assessment, Evaluation and Accountability, 21(1), 5–31. https://doi.org/10.1007/s11092-008-9068-5 DOI: https://doi.org/10.1007/s11092-008-9068-5

Shute, V. J. (2008). Focus on formative feedback. Review of Educational Research, 78(1), 153-189. https://doi.org/10.3102/0034654307313795 DOI: https://doi.org/10.3102/0034654307313795

Clark, I. (2012). Formative assessment: Assessment is for self-regulated learning. Educational Psychology Review, 24(2), 205–249. https://doi.org/10.1007/s10648-011-9191-6 DOI: https://doi.org/10.1007/s10648-011-9191-6

Heritage, M. (2010). Formative assessment: Making it happen in the classroom. Corwin Press. https://doi.org/10.4135/9781452219493 DOI: https://doi.org/10.4135/9781452219493

Black, P., & Wiliam, D. (2010). Inside the black box: Raising standards through classroom assessment. Phi Delta Kappan, 80(2), 139–148. DOI: https://doi.org/10.1177/003172171009200119

Siemens, G. (2013). Learning Analytics: The Emergence of a Discipline. American Behavioral Scientist, 57(10), 1380-1400. https://doi.org/10.1177/0002764213498851 DOI: https://doi.org/10.1177/0002764213498851

Song, C.; Shin, S.-Y.; Shin, K.-S. (2024). Implementing the Dynamic Feedback-Driven Learning Optimization Framework: A Machine Learning Approach to Personalize Educational Pathways. Applied Sciences. 14(2), 916. https://doi.org/10.3390/app14020916 DOI: https://doi.org/10.3390/app14020916

Williamson, B., & Piattoeva, N. (2018). Objectivity as standardization in data-scientific education policy, technology, and governance. Learning, Media and Technology, 44(1), 64–76. https://doi.org/10.1080/17439884.2018.1556215 DOI: https://doi.org/10.1080/17439884.2018.1556215

West, D. M., & Allen, J. (2018). How Artificial Intelligence Is Transforming the World. Brookings.

Selwyn, N. (2019). Should robots replace teachers? AI and the Future of Education. (1st ed.) Polity Press.

Mousa, M. & Molnár, G. (2019). Applying computer-based testing in Palestine: Assessing fourth and ﬁfth graders inductive reasoning. Journal of Studies in Education, 9(3), 1–13. https://doi.org/10.5296/jse.v9i3.14799 DOI: https://doi.org/10.5296/jse.v9i3.14799

Mousa, M. & Molnár, G. (2019). The feasibility of computer-based testing in Palestine among lower primary school students: Assessing mouse skills and inductive reasoning. Journal of Studies in Education, 9(2), 1–16. https://doi.org/10.5296/jse.v9i2.14517 DOI: https://doi.org/10.5296/jse.v9i2.14517

Mousa, M. & Molnár, G. (2020). Computer-Based Training in Math Improves Inductive Reasoning of 9- to 11-year-old Children. Thinking Skills and Creativity, 37. https://doi.org/10.1016/j.tsc.2020.100687 DOI: https://doi.org/10.1016/j.tsc.2020.100687

Hwang, G.-J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100001. https://doi.org/10.1016/j.caeai.2020.100001 DOI: https://doi.org/10.1016/j.caeai.2020.100001

Alazemi, A. F. T. (2024). Formative assessment in artificially integrated instruction: Delving into the effects on reading comprehension progress, online academic enjoyment, personal best goals, and academic mindfulness. Language Testing in Asia, 14(44). https://doi.org/10.1186/s40468-024-00319-8 DOI: https://doi.org/10.1186/s40468-024-00319-8

Ji H, Suo L and Chen H (2024) AI performance assessment in blended learning: mechanisms and effects on students’ continuous learning motivation. Front. Psychol. 15,1447680. https://doi.org/10.3389/fpsyg.2024.1447680 DOI: https://doi.org/10.3389/fpsyg.2024.1447680