Feedback That Clicks: Introductory Physics Students' Valued Features in AI Feedback Generated From Self-Crafted and Engineered Prompts
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866912580588535808 |
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| author | Sirnoorkar, Amogh Rebello, N. Sanjay |
| author_facet | Sirnoorkar, Amogh Rebello, N. Sanjay |
| contents | Since the advent of GPT-3.5 in 2022, Generative Artificial Intelligence (AI) has shown tremendous potential in STEM education, particularly in providing real-time, customized feedback to students in large-enrollment courses. A crucial skill that mediates effective use of AI is the systematic structuring of natural language instructions to AI models, commonly referred to as prompt engineering. This study has three objectives: (i) to investigate the sophistication of student-generated prompts when seeking feedback from AI on their arguments, (ii) to examine the features that students value in AI-generated feedback, and (iii) to analyze trends in student preferences for feedback generated from self-crafted prompts versus prompts incorporating prompt engineering techniques and principles of effective feedback. Results indicate that student-generated prompts typically reflect only a subset of foundational prompt engineering techniques. Despite this lack of sophistication, such as incomplete descriptions of task context, AI responses demonstrated contextual intuitiveness by accurately inferring context from the overall content of the prompt. We also identified 12 distinct features that students attribute the usefulness of AI-generated feedback, spanning four broader themes: Evaluation, Content, Presentation, and Depth. Finally, results show that students overwhelmingly prefer feedback generated from structured prompts, particularly those combining prompt engineering techniques with principles of effective feedback. Implications of these results such as integrating the principles of effective feedback in design and delivery of feedback through AI systems, and incorporating prompt engineering in introductory physics courses are discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08516 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Feedback That Clicks: Introductory Physics Students' Valued Features in AI Feedback Generated From Self-Crafted and Engineered Prompts Sirnoorkar, Amogh Rebello, N. Sanjay Physics Education Since the advent of GPT-3.5 in 2022, Generative Artificial Intelligence (AI) has shown tremendous potential in STEM education, particularly in providing real-time, customized feedback to students in large-enrollment courses. A crucial skill that mediates effective use of AI is the systematic structuring of natural language instructions to AI models, commonly referred to as prompt engineering. This study has three objectives: (i) to investigate the sophistication of student-generated prompts when seeking feedback from AI on their arguments, (ii) to examine the features that students value in AI-generated feedback, and (iii) to analyze trends in student preferences for feedback generated from self-crafted prompts versus prompts incorporating prompt engineering techniques and principles of effective feedback. Results indicate that student-generated prompts typically reflect only a subset of foundational prompt engineering techniques. Despite this lack of sophistication, such as incomplete descriptions of task context, AI responses demonstrated contextual intuitiveness by accurately inferring context from the overall content of the prompt. We also identified 12 distinct features that students attribute the usefulness of AI-generated feedback, spanning four broader themes: Evaluation, Content, Presentation, and Depth. Finally, results show that students overwhelmingly prefer feedback generated from structured prompts, particularly those combining prompt engineering techniques with principles of effective feedback. Implications of these results such as integrating the principles of effective feedback in design and delivery of feedback through AI systems, and incorporating prompt engineering in introductory physics courses are discussed. |
| title | Feedback That Clicks: Introductory Physics Students' Valued Features in AI Feedback Generated From Self-Crafted and Engineered Prompts |
| topic | Physics Education |
| url | https://arxiv.org/abs/2509.08516 |