An An AI-Based Training Management Model for Enhancing Elementary School Teachers’ Ability to Construct Numeracy Literacy Questions
DOI:
https://doi.org/10.59373/kharisma.v5i2.269Keywords:
Training Management Model, Numeracy Literacy, Artificial Intelligence, Teacher CompetenceAbstract
Curriculum expectations increasingly require primary school teachers to design reasoning-based numeracy literacy assessments aligned with higher-order thinking skills (HOTS), yet many educators lack the capacity to do so. Although generative artificial intelligence (AI) tools such as ChatGPT are increasingly adopted in education, validated training management models that systematically embed AI support across the assessment-development cycle remain scarce. To address this gap, this study developed and evaluated NULIQFORM, an AI-assisted training management model integrating ChatGPT-supported scaffolding across assessment planning, question development, and evaluation. Utilizing a Research and Development (R&D) approach grounded in the ADDIE framework, the model was implemented and evaluated via a quasi-experimental one-group pretest–posttest design involving 31 primary school teachers purposively selected from Grades 2 and 5 across Islamic and public schools in Waru Subdistrict, Indonesia. Feasibility was triangulated through evaluations by participants and the head of the Teacher Working Group (KKG), while effectiveness was evaluated through pre- and post-training competence scores analyzed descriptively and inferentially. Results demonstrate that NULIQFORM was rated feasible with minor revisions, and its implementation produced a statistically significant improvement in teachers’ assessment-development competence (t = −6.147, p < 0.001; d = 1.10), with mean scores rising from 7.03 to 9.35. Given the single-group design and localized sample, these findings provide preliminary evidence of short-term effectiveness and offer a practical framework for embedding generative AI into teacher professional development.
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