logo logo European Journal of Educational Research

EU-JER is is a, peer reviewed, online academic research journal.

Subscribe to

Receive Email Alerts

for special events, calls for papers, and professional development opportunities.

Subscribe

Publisher (HQ)

Eurasian Society of Educational Research
Eurasian Society of Educational Research
Christiaan Huygensstraat 44, Zipcode:7533XB, Enschede, THE NETHERLANDS
Eurasian Society of Educational Research
Headquarters
Christiaan Huygensstraat 44, Zipcode:7533XB, Enschede, THE NETHERLANDS
Research Article

Exploring the Role of Artificial Intelligence-Powered Facilitator in Enhancing Digital Competencies of Primary School Teachers

Thi Hong Chuyen Nguyen

This study aimed to investigate the relationship between teacher professional development, quality of lecture design, student engagement, teacher tech.

T

This study aimed to investigate the relationship between teacher professional development, quality of lecture design, student engagement, teacher technical skills, pedagogical content knowledge and teacher satisfaction in using Artificial Intelligence (AI)-Powered Facilitator for designing lectures. The study used a non-random sample technique, and 208 participants answered a survey via Google Form after one semester, using a 5-point Likert scale to rate their responses. The structural equation model was used to analyze the data, and six factors were included in the study. The study confirmed hypotheses that teacher professional development, quality of lecture design, student engagement, and pedagogical content knowledge have a positive effect on teacher satisfaction. However, the study also revealed that teacher technical skills have a negative effect on teacher satisfaction, and pedagogical content knowledge has no significant effect. The proposed conceptual model explained 55.7% of the variance in teacher satisfaction Theoretical and practical implications were also discussed. These findings provide insights into the factors that contribute to teacher satisfaction in utilizing AI-Powered Facilitator for designing lectures and could inform the development of effective teacher training programs.

Keywords: AI-powered facilitators, digital competencies, lecture design, teacher professional development, technological pedagogical content knowledge.

cloud_download PDF
Cite
Article Metrics
Views
479
Download
1166
Citations
Crossref
2

Scopus
0

References

Alhajri, A. J. (2022). Kuwaiti teachers' satisfaction with the social studies curriculum and their performance in the classroom. Journal of Educational and Social Research, 12(1), 355-370. https://doi.org/10.36941/jesr-2022-0028

Amador, J. M., Rogers, M. A. P., Hudson, R., Phillips, A., Carter, I., Galindo, E., & Akerson, V. L. (2022). Novice teachers’ pedagogical content knowledge for planning and implementing mathematics and science lessons. Teaching and Teacher Education, 115, Article 103736. https://doi.org/10.1016/j.tate.2022.103736

Anderson, J. C., & Gerbing, D. W. (1984). The effect of sampling error on convergence, improper solutions, and goodness-of-fit indices for maximum likelihood confirmatory factor analysis. Psychometrika, 49, 155-173. https://doi.org/10.1007/BF02294170

Andyani, H., Setyosari, P., Wiyono, B. B., & Djatmika, E. T. (2020). Does technological pedagogical content knowledge impact on the use of ict in pedagogy? International Journal of Emerging Technologies in Learning, 15(3), 126-139. https://doi.org/10.3991/ijet.v15i03.11690

Bagheri, M. M. (2015). Intelligent and adaptive tutoring systems: How to integrate learners. International Journal of Education, 7(2), 1-16. https://doi.org/10.5296/ije.v7i2.7079

Balbo Di Vinadio, T., van Noordt, C., Vargas Alvarez del Castillo, C., & Avila, R. (2022). Artificial intelligence and digital transformation: Competencies for civil servants (Working Group Report on AI Capacity Building). Broadband Commission for Sustainable Development. http://hdl.voced.edu.au/10707/634371

Boel, C., Rotsaert, T., Valcke, M., Rosseel, Y., Struyf, D., & Schellens, T. (2023). Are teachers ready to immerse? Acceptance of mobile immersive virtual reality in secondary education teachers. Research in Learning Technology, 31, Article 2855. https://doi.org/10.25304/RLT.V31.2855

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510

Chiu, T. K. F. (2022). Applying the self-determination theory (sdt) to explain student engagement in online learning during the covid-19 pandemic. Journal of Research on Technology in Education, 54(S1), S14–S30. https://doi.org/10.1080/15391523.2021.1891998

Chookaew, S., Howimanporn, S., Hutamarn, S., & Thongkerd, T. (2021). Perceptions of vocational education and training teachers with regard to an industrial robot training. TEM Journal, 10(3), 1149-1154. https://doi.org/10.18421/TEM103-19

Costley, J., Hughes, C., & Lange, C. (2017). The effects of instructional design on student engagement with video lectures at cyber universities. Journal of Information Technology Education: Research, 16, 189-207. https://doi.org/10.28945/3728

Devinder, K., & Datta, B. (2003). A study of the effect of perceived lecture quality on post‐lecture intentions. Work Study, 52(5), 234-243. https://doi.org/10.1108/00438020310485967

Dikmen, C. H., & Demirer, V. (2022). The role of technological pedagogical content knowledge and social cognitive variables in teachers’ technology integration behaviors. Participatory Educational Research, 9(2), 398-415. https://doi.org/10.17275/per.22.46.9.2

Donath, J. L., Lüke, T., Graf, E., Tran, U. S., & Götz, T. (2023). Does professional development effectively support the implementation of inclusive education? A meta-analysis. Educational Psychology Review, 35, Article 30. https://doi.org/10.1007/s10648-023-09752-2

Faltynkova, L., Simonova, I., & Kostolanyova, K. (2020). Perspectives on distance education in secondary and tertiary education. In C. Busch, M. Steinicke, & T. Wendler, Proceedings of the 19th European Conference on E-learning (ECEL 2020) (pp. 556-562). Academic Conferences & Publishing International.

Fernandes, G. W. R., Rodrigues, A. M., & Ferreira, C. A. (2020). Professional development and use of digital technologies by science teachers: A review of theoretical frameworks. Research in Science Education, 50, 673–708. https://doi.org/10.1007/s11165-018-9707-x

Galimullina, E. Z., Ljubimova, Е. M., Mukhametshina, D. R., & Sozontova, E. A. (2022). Analysis of requirements for the digital competence of a future teacher. European Journal of Educational Research, 11(3), 1729-1745. https://doi.org/10.12973/eu-jer.11.3.1729

Gess-Newsome, J., Taylor, J. A., Carlson, J., Gardner, A. L., Wilson, C. D., & Stuhlsatz, M. A. M. (2019). Teacher pedagogical content knowledge, practice, and student achievement. International Journal of Science Education, 41(7), 944-963. https://doi.org/10.1080/09500693.2016.1265158

Hair, J. F. (2009). Multivariate data analysis (7th ed.). Pearson.

Haug, B. S., & Mork, S. M. (2021). Taking 21st century skills from vision to classroom: What teachers highlight as supportive professional development in the light of new demands from educational reforms. Teaching and Teacher Education, 100, Article 103286. https://doi.org/10.1016/J.TATE.2021.103286

Herawati, R., Tjahjono, H. K., Qamari, I. N., & Wahyuningsih, S. H. (2022). Does teacher's willingness to change enhance professional competence? European Journal of Educational Research, 11(3), 1463-1474. https://doi.org/10.12973/eu-jer.11.3.1463

Ho, C.-L., & Au, W.-T. (2006). Teaching satisfaction scale - measuring job satisfaction of teachers. Educational and Psychological Measurement, 66(1), 172–185. https://doi.org/10.1177/0013164405278573

Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023). Effects of artificial intelligence–enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom. Computers and Education, 194, Article 104684. https://doi.org/10.1016/j.compedu.2022.104684

Hulela, K., Rammolai, M., & Mpatane, W. (2014). Assessment of computer technology availability, accessibility and usage by agricultural education student teachers in secondary schools in botswana. Educational Research and Reviews, 9(17), 610-617. https://doi.org/10.5897/err2014.1753

Hwang, H., & Takane, Y. (2014). Generalized structured component analysis: A component-based approach to structural equation modeling. CRC Press. https://doi.org/10.1201/b17872

Ilomäki, L., Paavola, S., Lakkala, M., & Kantosalo, A. (2016). Digital competence – an emergent boundary concept for policy and educational research. Education and Information Technologies, 21, 655-679. https://doi.org/10.1007/s10639-014-9346-4

Katai, Z., & Iclanzan, D. (2023). Impact of instructor on-slide presence in synchronous e-learning. Education and Information Technologies, 28, 3089–3115. https://doi.org/10.1007/s10639-022-11306-y

Klassen, R. M., Perry, N. E., & Frenzel, A. C. (2012). Teachers' relatedness with students: An underemphasized component of teachers' basic psychological needs. Journal of Educational Psychology, 104(1), 150–165. https://doi.org/10.1037/a0026253

Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). The Guilford Press.

Kock, N., & Hadaya, P. (2018). Minimum sample size estimation in pls‐sem: The inverse square root and gamma‐exponential methods. Information Systems Journal, 28(1), 227-261. https://doi.org/10.1111/isj.12131

Koskimäki, M., Mikkonen, K., Kääriäinen, M., Lähteenmäki, M.-L., Kaunonen, M., Salminen, L., & Koivula, M. (2021). Development and testing of the educators' professional development scale (eduprode) for the assessment of social and health care educators' continuing professional development. Nurse Education Today, 98, Article 104657. https://doi.org/10.1016/j.nedt.2020.104657

Kulkarni, A., & Eagle, M. (2020). Towards understanding the impact of real-time ai-powered educational dashboards (raed) on providing guidance to instructors. In A. N. Rafferty, J. Whitehill, Violetta, Cavalli-Sforza, & C. Romero (eds.), Proceedings of The 13th International Conference on Educational Data Mining (EDM 2020) (pp. 781-784). Educational Data Mining. https://bit.ly/3qWUkgR

Lameras, P., & Arnab, S. (2022). Power to the teachers: An exploratory review on artificial intelligence in education. Information (Switzerland), 13(1), Article 14. https://doi.org/10.3390/info13010014

Lange, C., & Costley, J. (2020). Improving online video lectures: Learning challenges created by media. International Journal of Educational Technology in Higher Education, 17, Article 16. https://doi.org/10.1186/s41239-020-00190-6

Laxmaiah, B., Ramji, B., & Kiran, A. U. (2022). Intelligent and adaptive learning management system technology (lmst) using data mining and artificial intelligence. In A. Kumar & S. E. Mozar. Lecture Notes in Electrical Engineering (pp. 333–341), https://doi.org/10.1007/978-981-16-7985-8_35

Liu, D., & Zhang, H. (2021). Developing a new model for understanding teacher satisfaction with online learning. Sage Open, 11(3), 1-16. https://doi.org/10.1177/21582440211036440

Maunula, M., Maunumäki, M., Marôco, J., & Harju-Luukkainen, H. (2023). Developing students well-being and engagement in higher education during covid-19—a case study of web-based learning in finland. Sustainability (Switzerland), 15(4), Article 3838. https://doi.org/10.3390/su15043838

Meyer, A., Kleinknecht, M., & Richter, D. (2023). What makes online professional development effective? The effect of quality characteristics on teachers’ satisfaction and changes in their professional practices. Computers & Education, 200, Article 104805. https://doi.org/10.1016/J.COMPEDU.2023.104805

Mo, H., & Yan, X. (2021). Problems and countermeasures of cultivating autonomous english learning ability in vocational colleges based on informationization. Journal of Contemporary Educational Research, 5(6), Article 2205. https://doi.org/10.26689/jcer.v5i6.2205

Mohammadreza, E., & Safabakhsh, R. (2021). Lecture quality assessment based on the audience reactions using machine learning and neural networks. Computers and Education: Artificial Intelligence, 2, Article 100022. https://doi.org/10.1016/j.caeai.2021.100022

Mowbray, R., & Perry, L. B. (2015). Improving lecture quality through training in public speaking. Innovations in Education and Teaching International, 52(2), 207-217. https://doi.org/10.1080/14703297.2013.849205

Nawi, A., Hamzah, M. I., Ren, C. C., & Tamuri, A. H. (2015). Adoption of mobile technology for teaching preparation in improving teaching quality of teachers. International Journal of Instruction, 8(2), 113-124. https://doi.org/10.12973/iji.2015.829a

Nazaretsky, T., Bar, C., Walter, M., & Alexandron, G. (2022). Empowering teachers with ai: Co-designing a learning analytics tool for personalized instruction in the science classroom. LAK22: 12th International Learning Analytics and Knowledge Conference. https://doi.org/10.1145/3506860.3506861  

Nguyen, V. T., Lai, H. T., & Ha, Q. V. (2023). Factors affecting the readiness of digital transformation adopters: A case study in vietnam. International Research Journal of Science, Technology, Education, & Management, 3(1), 31-42. https://doi.org/10.5281/zenodo.7772821

Nguyen, V. T., & Nguyen, C. T. H. (2022). A systematic review of structural equation modeling in augmented reality applications. Indonesian Journal of Electrical Engineering and Computer Science, 28(1), 328-338. https://doi.org/10.11591/ijeecs.v28.i1.pp328-338

Nguyen, V. T., & Nguyen, C. T. H. (2023). The effect of structural equation modeling on chatbot usage: An investigation of Dialogflow. International Journal of Applied Information Technology, 6(1), 38-49. https://doi.org/10.25124/ijait.v6i01.4840

Orhan, G., & Beyhan, Ö. (2020). Teachers' perceptions and teaching experiences on distance education through synchronous video conferencing during covid-19 pandemic. Social Sciences and Education Research Review, 7(1), 8-44. https://EconPapers.repec.org/RePEc:edt:jsserr:v:7:y:2020:i:1:p:8-44

Ouabich, R., Tifroute, L., & Bounabe, A. (2023). Science awareness: Analysis of moroccan curriculum framework for preschool education. European Journal of Educational Research, 12(3), 1233-1246. https://doi.org/10.12973/EU-JER.12.3.1233

Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, Article 100020. https://doi.org/10.1016/j.caeai.2021.100020

Pedler, M., Hudson, S., & Yeigh, T. (2020). The teachers' role in student engagement: A review. Australian Journal of Teacher Education, 45(3), Article 4. https://doi.org/10.14221/ajte.2020v45n3.4

Postholm, M. B. (2012). Teachers' professional development: A theoretical review. Educational Research, 54(4), 405-429 https://doi.org/10.1080/00131881.2012.734725

Sadaf, M. (2019). Measuring the impact of technological pedagogical content knowledge on teacher resilience in universities of pakistan. International Journal of Management Excellence, 12(3), 1872-1881. https://bit.ly/3sBMidH

Salim, H., Waterworth, P. G., Daud, A., Dahnilsyah, & Hanif, M. (2023). The integration of digital technologies into practicum classrooms by smartphone-savvy pre-service teachers in indonesia. European Journal of Educational Research, 12(2), 593-603. https://doi.org/10.12973/eu-jer.12.2.593

Singh, A., Karayev, S., Gutowski, K., & Abbeel, P. (2017). Gradescope: A fast, flexible, and fair system for scalable assessment of handwritten work. Proceedings of the Fourth (2017) ACM Conference on Learning @ Scale (pp. 81-88), USA. https://doi.org/10.1145/3051457.3051466

Wen-Jing, W. (2018). Improved adaptive genetic algorithm for course scheduling in colleges and universities. International Journal of Emerging Technologies in Learning, 13(6), 29–42. https://doi.org/10.3991/ijet.v13i06.8442

Yeoun, M.-H., & Jung, E. T. (2021). An exploratory experiment on the possibility of ai-powered logo design tool. Design Convergence Study, 20(2), 114-129. https://doi.org/10.31678/sdc87.7

...