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Exploring the Effectiveness of AI Course Assistants on the Student Learning Experience Cover

Exploring the Effectiveness of AI Course Assistants on the Student Learning Experience

Open Access
|Nov 2024

References

  1. Al-Abdullatif, A. M. (2023). Modeling students’ perceptions of chatbots in learning: Integrating technology acceptance with the value-based adoption model. Education Sciences, 13(11), 1151. 10.3390/educsci13111151
  2. Artino, A. R., Jr. (2005). Review of the motivated strategies for learning questionnaire. ERIC. https://files.eric.ed.gov/fulltext/ED499083.pdf
  3. Baker, R., & Siemens, G. (2014). Educational data mining and learning analytics. In K. R. Sawyer (Ed.), The Cambridge Handbook of the Learning Sciences (2nd ed., pp. 253272). Cambridge University Press. 10.1017/CBO9781139519526.016
  4. Bandura, A. (1997). Self-efficacy: The exercise of control. Freeman.
  5. Bozkurt, A. (2023). Generative AI, synthetic contents, open educational resources (OER), and open educational practices (OEP): A new front in the openness landscape. Open Praxis, 15(3), 178184. 10.55982/openpraxis.15.3.579
  6. Bozkurt, A. (2024a). Why generative AI literacy, why now, and why it matters in the educational landscape? Kings, queens, and GenAI dragons. Open Praxis, 16(3), 283290. 10.55982/openpraxis.16.3.739
  7. Bozkurt, A. (2024b). GenAI et al.: Cocreation, authorship, ownership, academic ethics and integrity in a time of generative AI. Open Praxis, 16(1), 110. 10.55982/openpraxis.16.1.654
  8. Chen, X., Zou, D., Cheng, G., & Xie, H. (2022). AI in education: A review of current research and applications. Journal of Educational Technology & Society, 25(1), 3144.
  9. Cliff, N. (1993). Dominance statistics: Ordinal analyses to answer ordinal questions. Psychological Bulletin, 114(3), 494509. 10.1037/0033-2909.114.3.494
  10. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates.
  11. Deci, E. L., Vallerand, R. J., Pelletier, L. G., & Ryan, R. M. (1991). Motivation and Education: The Self-Determination Perspective. Educational Psychologist, 26(3–4), 325346. 10.1080/00461520.1991.9653137
  12. Deng, X., & Yu, Z. (2023). A meta-analysis and systematic review of the effect of chatbot technology use in sustainable education. Sustainability, 15(4), 2940. 10.3390/su15042940
  13. Duncan, T. G., & McKeachie, W. J. (2005). The Making of the Motivated Strategies for Learning Questionnaire. Educational Psychologist, 40(2), 117128. 10.1207/s15326985ep4002_6
  14. Essel, H. B., Vlachopoulos, D., Tachie-Menson, A., Johnson, E. E., & Baah, P. K. (2022). The impact of a virtual teaching assistant (chatbot) on students’ learning in Ghanaian higher education. International Journal of Educational Technology in Higher Education, 19(1), 57. 10.1186/s41239-022-00362-6
  15. Fidan, M., & Gencel, N. (2022). Supporting instructional videos with chatbot and peer feedback mechanisms in online learning: The effects on learning performance and intrinsic motivation. Journal of Educational Computing Research, 60(7), 17161741. 10.1177/07356331221077901
  16. Field, A. P. (2018). Discovering Statistics Using IBM SPSS Statistics. 5th Edition, Sage, Newbury Park.
  17. Grassini, S. (2023). Shaping the future of education: Exploring the potential and consequences of AI and ChatGPT in educational settings. Education Sciences, 13(7), 692. 10.3390/educsci13070692
  18. Hanshaw, G., & Miller, K. (2024). Evaluating the impact of real-time AI feedback on student writing: Randomized control trial. Manuscript in preparation. Los Angeles Pacific University.
  19. Hartnett, M., St. George, A., & Dron, J. (2011). Examining motivation in online distance learning environments: Complex, multifaceted, and situation-dependent. The International Review of Research in Open and Distributed Learning, 12(6), 2038. 10.19173/irrodl.v12i6.1030
  20. Ilieva, G., Yankova, T., Klisarova-Belcheva, S., Dimitrov, A., Bratkov, M., & Angelov, D. (2023). Effects of generative chatbots in higher education. Information, 14(9), 492. 10.3390/info14090492
  21. Kearsley, G., & Shneiderman, B. (1998). Engagement theory: A framework for technology-based teaching and learning. Educational Technology, 38(5), 2023. https://www.jstor.org/stable/44428478
  22. Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: Systematic literature review. International Journal of Educational Technology in Higher Education, 20(1), 117. 10.1186/s41239-023-00426-1
  23. Lee, J. (2014). An exploratory study of effective online learning: Assessing satisfaction levels of graduate students of mathematics education associated with human and design factors of an online course. The International Review of Research in Open and Distributed Learning, 15(1), 111132. 10.19173/irrodl.v15i1.1638
  24. Maphoto, K. B., Sevnarayan, K., Mohale, N. E., Suliman, Z., Ntsopi, T. J., & Mokoena, D. (2024). Advancing students’ academic excellence in distance education: Exploring the potential of generative AI integration to improve academic writing skills. Open Praxis, 16(2), 142159. 10.55982/openpraxis.16.2.649
  25. Parsakia, K. (2023). The effect of chatbots and AI on the self-efficacy, self-esteem, problem-solving, and critical thinking of students. Health Nexus, 1(1), 7176. 10.61838/hn.1.1.14
  26. Pintrich, P. R., Smith, D. A. F., Duncan, T., & McKeachie, W. J. (1993). Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801813. 10.1177/0013164493053003024
  27. Pintrich, P. R., Smith, D. A. F., García, T., & McKeachie, W. J. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ). National Center for Research to Improve Postsecondary Teaching and Learning. https://files.eric.ed.gov/fulltext/ED338122.pdf
  28. Razali, N., & Wah, Y. (2011). Power Comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests. Journal of Statistical Modeling and Analytics, 2, 2133.
  29. Richardson, J. C., Maeda, Y., Lv, J., & Caskurlu, S. (2017). Social presence in relation to students’ satisfaction and learning in the online environment: A meta-analysis. Computers in Human Behavior, 71, 402417. 10.1016/j.chb.2017.02.001
  30. Romano, J., & Kromrey, J. (2006). Appropriate Statistics for Ordinal Level Data: Should We Really Be Using t-test and Cohen’s d for Evaluating Group Differences on the NSSE and other Surveys?
  31. Ryan, R. M., & Deci, E. L. (2000). Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary Educational Psychology, 25(1), 5467. 10.1006/ceps.1999.1020
  32. Schiefele, U. (1991). Interest, learning, and motivation. Educational Psychologist, 26(3–4), 299323. 10.1080/00461520.1991.9653136
  33. Schunk, D. H., & Pajares, F. (2002). The Development of Academic Self-Efficacy. In A. Wigfield, & J. S. Eccles (Eds.), Development of Achievement Motivation (pp. 1531). Academic Press. 10.1016/B978-012750053-9/50003-6
  34. Schwarzer, R., & Jerusalem, M. (1995). Generalized Self-Efficacy Scale. In J. Weinman, S. Wright, & M. Johnston (Eds.), Measures in health psychology: A user’s portfolio. Causal and control beliefs (pp. 3537). Nfer-Nelson. 10.1037/t00393-000
  35. Smith, L. (2019). Addressing the equity gap in education: Strategies for success. Journal of Educational Research, 112(4), 495509.
  36. Smith, R., Smith, E., & Price, M. D. (2024). Utilizing emergent AI chatbot technology to generate mathematical writing models for elementary students with learning disabilities. *Intervention in School. 10.1177/10534512241233512
  37. Sublett, C. (2020). Distant equity: The promise and pitfalls of online learning for students of color in higher education. American Council on Education. https://www.equityinhighered.org/wp-content/uploads/2020/11/c.-sublett-essay-final.pdf
  38. Van den Berg, G. (2024). Generative AI and educators: Partnering in using open digital content for transforming education. Open Praxis, 16(2), 130141. 10.55982/openpraxis.16.2.640
  39. West, D. M., & Bleiberg, J. (2013). Five ways technology can close the achievement gap in American schools. Center for Technology Innovation at Brookings. https://www.brookings.edu/research/five-ways-technology-can-close-the-achievement-gap-in-american-schools/
  40. Williams, R. T. (2024). The ethical implications of using generative chatbots in higher education. Frontiers in Education, 8, 1331607. 10.3389/feduc.2023.1331607
  41. Wu, R., & Yu, Z. (2023). Do AI chatbots improve students’ learning outcomes? Evidence from a meta-analysis. British Journal of Educational Technology, 55(1), 1033. 10.1111/bjet.13334
  42. Zhao, X., Zhang, G., & Xiong, W. (2023). The impact of chatbot-assisted instructional videos and micro-learning systems on intrinsic motivation in education. Sustainability, 15(4), 2940. 10.3390/su15042940
Language: English
Page range: 627 - 644
Submitted on: Jun 22, 2024
Accepted on: Sep 29, 2024
Published on: Nov 29, 2024
Published by: International Council for Open and Distance Education (ICDE)
In partnership with: Paradigm Publishing Services

© 2024 George Hanshaw, Joanna Vance, Craig Brewer, published by International Council for Open and Distance Education (ICDE)
This work is licensed under the Creative Commons Attribution 4.0 License.