The method of assessment affects on learning by determining how students manage their time and prioritize subjects. It is widely accepted that students may demonstrate different skills in different assessment formats. The authors demonstrated how e-assessment through the Halomda educational platform can not only improve student learning outcomes, but also enrich their learning experiences. In addition, it is shown how ChatGPT integrated with two new math exploration tools into proprietary Chat-Mat™ module, can help students learn at home and in the classroom, as well as support teachers in their daily work of reviewing student assignments. The outcomes of teaching courses with Halomda not only reveal impressive student performance on final exams but also illustrate a strong correlation between exam scores and weekly assignment grades.

1. P.E. Rawlusyk, “Assessment in Higher Education and Student Learning”,  Journal of Instructional Pedagogies, vol. 21, pp. 1-34, 2018.  https://bit.ly/2vPtbzT.
2. J. Biggs, C. Tang, Teaching for Quality Learning at University: What the student does, (3rd ed). Maidenhead: McGraw-Hill, 2007.
3. G. Brown, J. Bull, M. Pendlebury, Assessing student learning in higher education, London: Routledge, 1997.
4. R. De Villiers, J. Scott-Kennel, R. Larke, “Principles of effective e-assessment: A proposed framework”, Journal of International Business Education, vol. 11, pp. 65–92, 2016.
5. M. Greenhow, “Embedding e-assessment in an effective way”, MSOR Connections, vol. 17, no. 3, – journals.gre.ac.uk., 2019.
6. B. Eager, R. Brunton, “Prompting Higher Education Towards AI-Augmented Teaching and Learning Practice”, Journal of University Teaching & Learning Practice, vol. 20, no. 5, 2023, https://doi.org/10.53761/ 1.20.5.02.
7. P. Drijvers, “Digital assessment of mathematics: Opportunities, issues and criteria”, Mesure et évaluation en éducation, vol. 41, no. 1, 41-66, 2018, https://doi.org/10.7202/1055896ar.
8. H. Mellar et al.,” Addressing cheating in e-assessment using student authentication and authorship checking systems: teachers’ perspectives,” Int. Journal for Educational Integrity, vol. 14, no. 2, 2018. DOI 10.1007/s40979-018-0025-x.
9. R. Peytcheva-Forsyth, L. Aleksieva, B. Yovkova, “The Impact of Technology on Cheating and Plagiarism in the Assessment – the teachers’ and students’ perspectives”, AIP Conference Proceedings 2048, 020037, 2018, https://doi.org/10.1063/1.5082055.
10. J. Dermo, “E-assessment and the student learning experience: A survey of student perceptions of e-assessment“, British Journal of Educational Technology, vol. 40, no. 2, pp. 203–214, 2009, https://doi.org/10.1111/j.1467-8535.2008.00915.x.
11. S. Kocdar, A. Karadeniz, R. Peytcheva-Forsyth, and V. Stoeva, “Cheating and Plagiarism in E-Assessment: Students’ Perspectives”, Open Praxis, vol. 10 issue 3, July–September 2018, pp. 221–235 (ISSN 2304-070X).
12. M. E. Rodríguez, A.-E. Guerrero-Roldán, D. Baneres, I. Noguera, “Students’ Perceptions of and Behaviors Toward Cheating in Online Education“, IEEE Revista Iberoamericana De Tecnologias Del Aprendizaje, vol. 16, no. 2, May 2021. Doi: 10.1109/RITA.2021.3089925.
13. A. Vegendla,  G. Sindre, “Mitigation of Cheating in Online Exams: Strengths and Limitations of Biometric Authentication”, Biometric Authentication in Online Learning Environments, 2019, DOI: 10.4018/978-1-5225-7724-9.ch003.
14. J. M. Bartley, Assessment is as Assessment does: A conceptual framework for understanding online assessment and measurement, Online assessment and measurement: Foundations and challenges, pp. 1–45, 2005, IGI Global.
15. S. Stacey, “Cheating on your college essay with ChatGPT”, Business Insider, https://www.businessinsider. com/professors-say-chatgpt-wont-kill-college-essays-make-education-fairer 2022-12.
16. M. Ivanova, S. Bhattacharjee, S. Marcel, A. Rozeva,  M. Durcheva, “Enhancing Trust in eAssessment – the TeSLA System Solution”, 21st Conf. on e-assessment (TEA2018),10-11 Dec 2018, Amsterdam, The Netherlands, Conf. Proc., https://www.teaconference.org/. arXiv:1905.04985.
17. M. Durcheva et al., “Innovations in teaching and assessment of engineering courses, supported by authentication and authorship analysis system“, The 45th conference AMEE’19, AIP Conf. Proc. 2172, 040004 (2019); https://doi.org/10.1063/1.5133514.
18. J. M. Azevedo, E.P. Oliveira, P.D. Beites, “How Do Mathematics Teachers in Higher Education Look at E-assessment with Multiple-Choice Questions”, International Conference on Computer Supported Education, 2017, DOI:10.21913/IJEI.V3I2.165.
19. S. Zegowitz, “Evaluating the use of e-assessment in a first-year pure mathematics module”, Preprint, 2022, https://arxiv.org/pdf/1908.01028.pdf.
20. S. Trenholm, “An investigation of assessment in fully asynchronous online math courses”, The International Journal for Educational Integrity, 3‘, 2007, DOI:10.21913/IJEI.V3I2.165.
21. STACK, https://stack-assessment.org.
22. WebAssign,  https://webassign.com.
23. WeBWorK, https://webwork.maa.org.
24. Numbas. https://www.numbas.org.uk.
25. C. J. Sangwin, Computer Aided Assessment of Mathematics. Oxford: Oxford University Press, 2013.
26. D. Serhan and F. Almeqdadi, “Students’ perceptions of using MyMathLab and WebAssign in mathematics classroom”, International Journal of Technology in Education and Science (IJTES), vol. 4, no.1, pp. 12-17, 2020.
27. A. G. d’Entremont, P. J. Walls,  P. A. Cripton, “Student Feedback and Problem Development for WeBWorK in a Second-Year Mechanical Engineering Program”. Proc. 2017 Canadian Engineering Education Association (CEEA17) Conf.
28. V. Challis, R. Cook, and P. Anand, “Priming Numbas for formative assessment in a first-year mathematics unit”, In Gregory, S., Warburton, S. & Schier, M. (Eds.), Back to the Future – ASCILITE ‘21. Proc. ASCILITE 2021 in Armidale (pp. 313–318), (2021), https://doi.org/10.14742/ascilite2021.0145.
29. Halomda. https://halomda.org.
30. S. Kornstein, “Xpress Formula Editor and Symbolic Calculator”, Mathematics Teacher, vol. 94, no. 5, May 2001.
31. P. Slobodsky, M. Durcheva, “E-assessment of Mathematics   Courses using the Halomda Platform”, AIP Conf. Proc. 2939, 050011, 2023, https://doi.org/10.1063/5.0178514.
32. Y. Wardat, M. Tashtoush, R. AlAli, A. Jarrah, “ChatGPT: A revolutionary tool for teaching and learning mathematics”, Eurasia Journal of Mathematics, Science and Technology Education, vol. 19, no. 7, em2286, 2023, https://doi.org/10.29333/ejmste/13272.
33. S. Zhao, Y. Shen,  Z. Qi, “Research on ChatGPT-Driven Advanced Mathematics Course”,  Academic Journal of Mathematical Sciences, vol. 4, no. 5, 2023, doi: 10.25236/AJMS.2023.040506.
34. Rane, Nitin, “Enhancing Mathematical Capabilities through ChatGPT and Similar Generative Artificial Intelligence: Roles and Challenges in Solving Mathematical Problems”, 2023,  http://dx.doi.org/10.2139/ssrn.4603237.
35. J. Velásquez-Henao, C.J. Franco-Cardona, L. Cadavid-Higuita, “Prompt Engineering: a methodology for optimizing interactions with AI-Language Models in the field of engineering”, DYNA, 90 (230), Especial Conmemoración 90 años, pp. 9-17, Nov 2023, ISSN 0012-7353, DOI: https://doi.org/10.15446/dyna.v90n230.111700.
36. P. Slobodsky, A. Shtarkman, A. Kouropatov, “The implementation of e-training and e-assessment system Math-Xpres in development and practical use of system Math-Xpres in development and practical use of math courses at teachers’ colleges in Israel during the Corona times”, (Unpublished communication), 2022.

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