Strengthening Junior High School Teachers' Competence in Responsive Digital Assessment through AI-Based Automatic Assessment (AABAI)
DOI:
https://doi.org/10.26740/jodic.v4n2.p1-14Keywords:
Artificial intelligence, Responsive assessment, Teacher competence, Digital transformationAbstract
Digital transformation encourages teachers to develop assessment methods that are not only efficient but also capable of generating data-driven diagnostic feedback. This community service initiative aims to enhance the knowledge and skills of junior high school teachers in Bengkulu City regarding the design and implementation of AI-based automatic assessment (AABAI). The program employed a Community-Based Participatory Research approach, involving 25 public junior high school teachers as participatory partners. The activity stages included needs assessment, product dissemination, an instrument development workshop, hands-on AABAI practice, implementation support, and monitoring and evaluation. Data were collected using knowledge tests, skill observation sheets, product quality rubrics, response questionnaires, interviews, and system usage documentation. Twenty-five junior high school teachers from across Bengkulu City participated in the program. The results demonstrate improved participant understanding through practice-based mentoring and reflection. This initiative produced digital assessment instruments and a community of practice that can be sustained through a partnership between the university and the Bengkulu City Education Office
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Almasri, F. (2024). Exploring the Impact of Artificial Intelligence in Teaching and Learning of Science: A Systematic Review of Empirical Research. Research in Science Education, 54(5), 977–997. https://doi.org/10.1007/s11165-024-10176-3
Alonzo, D., Quimno, V., Townend, G., & Oo, C. Z. (2024). Using information and communication technology (ICT)-based data systems to support teacher data-driven decision-making: Insights from the literature (2013–2023). Educational Assessment, Evaluation and Accountability, 36(4), 433–451. https://doi.org/10.1007/s11092-024-09443-8
Banitalebi, Z., Estaji, M., & Brown, G. T. L. (2025). Measuring teacher assessment literacy in digital environments: Development and validation of a scenario-based instrument. Educational Technology and Society, 28(2), 169–215. https://doi.org/10.30191/ETS.202504_28(2).RP10
Black, P., & Wiliam, D. (1998). Assessment and classroom learning. In International Journal of Phytoremediation (Vol. 21, Number 1). https://doi.org/10.1080/0969595980050102
Chiu, T. K. F., Ahmad, Z., & Çoban, M. (2025). Development and validation of teacher artificial intelligence (AI) competence self-efficacy (TAICS) scale. Education and Information Technologies, 30(5), 6667–6685. https://doi.org/10.1007/s10639-024-13094-z
Clark, A. K. (2022). Teacher Assessment Literacy : Implications for Diagnostic Assessment Systems University of Kansas 1122 W Campus Road , Lawrence , KS 66045 ORCiD : 0000-0002-5804-8336 Twitter : @ atlas4learning University of Kansas 1122 W Campus Road , Lawrence , KS 66045. 1–32.
Gao, R., Merzdorf, H. E., Anwar, S., Hipwell, M. C., & Srinivasa, A. R. (2024). Automatic assessment of text-based responses in post-secondary education: A systematic review. Computers and Education: Artificial Intelligence, 6, 100206. https://doi.org/https://doi.org/10.1016/j.caeai.2024.100206
Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81–112. https://doi.org/10.3102/003465430298487
Israel, B. A., Schulz, A. J., Becker, A. B., Reyes, C. L., Parker, E. A., & Reyes, A. G. (2026). Community-Based Participatory Research: Evolution and Significant Developments. Annual Review of Public Health, 47(1), 135–157. https://doi.org/10.1146/annurev-publhealth-082224-022223
Israel, B. A., Schulz, A. J., Parker, E. A., & Becker, A. B. (1998). Review of community-based research: Assessing partnership approaches to improve public health. Annual Review of Public Health, 19, 173–202. https://doi.org/10.1146/annurev.publhealth.19.1.173
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence Unleashed. In Open Ideas at Pearson. https://discovery.ucl.ac.uk/id/eprint/1475756/
Mandailina, V., Syaharuddin, S., & Abdillah, A. (2024). Pelatihan Penerapan Teknologi Artificial Intelligence untuk Meningkatkan Kompetensi Guru dalam Menyusun Perangkat Pembelajaran. Darma Diksani: Jurnal Pengabdian Ilmu Pendidikan, Sosial, Dan Humaniora, 4(1), 26–37. https://doi.org/10.29303/darmadiksani.v4i1.4928
Martin, F., Kim, S., Bolliger, D. U., & DeLarm, J. (2025). Assessment Types, Strategies, and Feedback in Online Higher Education Courses in the Age of Artificial Intelligence: Perspectives of Instructional Designers. TechTrends, 69(6), 1330–1346. https://doi.org/10.1007/s11528-025-01115-8
Morris, R., Gorard, S., See, B. H., & Siddiqui, N. (2024). Can a code-based approach to marking and feedback reduce teachers’ workload? An evaluation of the FLASH marking intervention. Oxford Review of Education, 50(4), 552–569. https://doi.org/10.1080/03054985.2023.2258779
Mustafa, M. Y., Tlili, A., Lampropoulos, G., Huang, R., Jandrić, P., Zhao, J., Salha, S., Xu, L., Panda, S., Kinshuk, López-Pernas, S., & Saqr, M. (2024). A systematic review of literature reviews on artificial intelligence in education (AIED): a roadmap to a future research agenda. In Smart Learning Environments (Vol. 11, Number 1). Springer Nature Singapore. https://doi.org/10.1186/s40561-024-00350-5
Nguyen, L. A. T., & Habók, A. (2024). Tools for assessing teacher digital literacy: a review. In Journal of Computers in Education (Vol. 11, Number 1). Springer Berlin Heidelberg. https://doi.org/10.1007/s40692-022-00257-5
Nicol, D., & MacFarlane-Dick, D. (2006). Formative assessment and selfregulated learning: A model and seven principles of good feedback practice. Studies in Higher Education, 31(2), 199–218. https://doi.org/10.1080/03075070600572090
Sadler, D. R. (1998). Formative assessment: Revisiting the territory. International Journal of Phytoremediation, 21(1), 77–84. https://doi.org/10.1080/0969595980050104
UNESCO. (2023). Guidance for generative AI in education and research. In Guidance for generative AI in education and research. https://doi.org/10.54675/ewzm9535
Varier, D., & Yun, S. (2023). A review of research on assessment use: implications for preparing early childhood educators for data-informed decision-making. Journal of Early Childhood Teacher Education, 44(4), 1067–1092. https://doi.org/10.1080/10901027.2023.2257157
Vittorini, P., Menini, S., & Tonelli, S. (2021). An AI-Based System for Formative and Summative Assessment in Data Science Courses. International Journal of Artificial Intelligence in Education, 31(2), 159–185. https://doi.org/10.1007/s40593-020-00230-2
Walter, Y. (2024). Embracing the future of Artificial Intelligence in the classroom: the relevance of AI literacy, prompt engineering, and critical thinking in modern education. International Journal of Educational Technology in Higher Education, 21(1). https://doi.org/10.1186/s41239-024-00448-3
Webb, M., & Galamba, A. (2026). Transforming pedagogy with GenAI-supported formative assessment: Challenges for teacher education. British Journal of Educational Technology, 57(3), 690–706. https://doi.org/10.1111/bjet.70051
Yim, I. H. Y., & Su, J. (2025). Artificial intelligence (AI) learning tools in K-12 education: A scoping review. In Journal of Computers in Education (Vol. 12, Number 1). Springer Berlin Heidelberg. https://doi.org/10.1007/s40692-023-00304-9
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). https://doi.org/10.1186/s41239-019-0171-0
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