← 研踪 PaperTrailarXiv:2611.15686 · v1
Integrating Slides, Mathematics, and Simulation in One Page: GenAI-Assisted HTML Learning Objects for Engineering Courses
首次提交 2026-11-12 00:27
版本更新 2026-11-12 00:27
雷达观测 2026-11-13 14:16
摘要
Engineering courses spread their materials across slides (PPTX), typeset notes (LaTeX/PDF), and MATLAB, leaving students to connect them. We describe an instructor-in-the-loop workflow in which generative AI (GenAI) converts these assets into single-file HTML learning objects that combine presentation, mathematics, visualization, simulation, and assessment; a page is released only after its simulations pass numerical verification. Six open demonstrators implement 18 tasks from six engineering courses. In the eight tasks with quantitative cross-checks, every quantity met its tolerance at default settings (1\%, or 2\% for the finite-difference time-domain simulation) and agreed with a separate Python re-computation and a value-blinded MATLAB re-computation run by a colleague not involved in development; the checks also flagged under-resolved settings. In a perception survey, students (19 after post-hoc screening of 28 responses) reported that the pages linked equations with simulation, and 74\% preferred them to separate files, but usability was marginal (System Usability Scale 56.8). Twelve lecturers rated the approach as useful (item means 4.25--4.58) but were hesitant to adopt it; their median ratings on a 12-criterion capability rubric agreed moderately with the first author's (; individual median 0.31). The results show technical feasibility and preliminary positive perceptions; learning outcomes were not measured.
作者备注：25 pages, 5 figures, 11 tables
观测到的版本
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v1 · 2026-11-12 00:27 · 最新观测版本
Integrating Slides, Mathematics, and Simulation in One Page: GenAI-Assisted HTML Learning Objects for Engineering Courses
Engineering courses spread their materials across slides (PPTX), typeset notes (LaTeX/PDF), and MATLAB, leaving students to connect them. We describe an instructor-in-the-loop workflow in which generative AI (GenAI) converts these assets into single-file HTML learning objects that combine presentation, mathematics, visualization, simulation, and assessment; a page is released only after its simulations pass numerical verification. Six open demonstrators implement 18 tasks from six engineering courses. In the eight tasks with quantitative cross-checks, every quantity met its tolerance at default settings (1\%, or 2\% for the finite-difference time-domain simulation) and agreed with a separate Python re-computation and a value-blinded MATLAB re-computation run by a colleague not involved in development; the checks also flagged under-resolved settings. In a perception survey, students (19 after post-hoc screening of 28 responses) reported that the pages linked equations with simulation, and 74\% preferred them to separate files, but usability was marginal (System Usability Scale 56.8). Twelve lecturers rated the approach as useful (item means 4.25--4.58) but were hesitant to adopt it; their median ratings on a 12-criterion capability rubric agreed moderately with the first author's (; individual median 0.31). The results show technical feasibility and preliminary positive perceptions; learning outcomes were not measured.
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