Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.03249v1</id>\n    <title>Using Learning Progressions to Guide AI Feedback for Science Learning</title>\n    <updated>2026-03-03T18:39:58Z</updated>\n    <link href='https://arxiv.org/abs/2603.03249v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.03249v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>Generative artificial intelligence (AI) offers scalable support for formative feedback, yet most AI-generated feedback relies on task-specific rubrics authored by domain experts. While effective, rubric authoring is time-consuming and limits scalability across instructional contexts. Learning progressions (LP) provide a theoretically grounded representation of students' developing understanding and may offer an alternative solution. This study examines whether an LP-driven rubric generation pipeline can produce AI-generated feedback comparable in quality to feedback guided by expert-authored task rubrics. We analyzed AI-generated feedback for written scientific explanations produced by 207 middle school students in a chemistry task. Two pipelines were compared: (a) feedback guided by a human expert-designed, task-specific rubric, and (b) feedback guided by a task-specific rubric automatically derived from a learning progression prior to grading and feedback generation. Two human coders evaluated feedback quality using a multi-dimensional rubric assessing Clarity, Accuracy, Relevance, Engagement and Motivation, and Reflectiveness (10 sub-dimensions). Inter-rater reliability was high, with percent agreement ranging from 89% to 100% and Cohen's kappa values for estimable dimensions (kappa = .66 to .88). Paired t-tests revealed no statistically significant differences between the two pipelines for Clarity (t1 = 0.00, p1 = 1.000; t2 = 0.84, p2 = .399), Relevance (t1 = 0.28, p1 = .782; t2 = -0.58, p2 = .565), Engagement and Motivation (t1 = 0.50, p1 = .618; t2 = -0.58, p2 = .565), or Reflectiveness (t = -0.45, p = .656). These findings suggest that the LP-driven rubric pipeline can serve as an alternative solution.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CL'/>\n    <published>2026-03-03T18:39:58Z</published>\n    <arxiv:comment>15pages, 4 figures</arxiv:comment>\n    <arxiv:primary_category term='cs.CL'/>\n    <author>\n      <name>Xin Xia</name>\n      <arxiv:affiliation>University of Georgia</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Nejla Yuruk</name>\n      <arxiv:affiliation>Gazi University</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Yun Wang</name>\n      <arxiv:affiliation>University of Georgia</arxiv:affiliation>\n    </author>\n    <author>\n      <name>Xiaoming Zhai</name>\n      <arxiv:affiliation>University of Georgia</arxiv:affiliation>\n    </author>\n  </entry>"
}