AI-based teaching evaluations: How well do they reflect student perceptions?
This study presents an innovative solution for evaluating university-level teaching quality using artificial intelligence (AI), focusing on key aspects such as clarity of explanation and lecture structure. Traditional student surveys, while valuable, are often subject to biases and lack the necessar...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-12-01
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| Series: | Computers and Education: Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X25000888 |
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| _version_ | 1850071372125962240 |
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| author | Yossi Ben Zion Shir Yakov Einat Abramovitch Gal Balter Nitza Davidovitch |
| author_facet | Yossi Ben Zion Shir Yakov Einat Abramovitch Gal Balter Nitza Davidovitch |
| author_sort | Yossi Ben Zion |
| collection | DOAJ |
| description | This study presents an innovative solution for evaluating university-level teaching quality using artificial intelligence (AI), focusing on key aspects such as clarity of explanation and lecture structure. Traditional student surveys, while valuable, are often subject to biases and lack the necessary granularity, creating a need for objective, scalable solutions that provide consistent results. We propose an automated framework utilizing advanced natural language processing (NLP) models to assess teaching quality based on lecture transcripts. The methodology combines AI-driven transcription, machine learning-based assessments, and correlation with institutional student evaluations to deliver reliable and reproducible measures of teaching effectiveness. The study analyzes 32 courses from 2017 to 2023, covering 1,222 hours of lecture video, and finds that AI assessments align significantly with student evaluations, particularly in terms of lecture structure and logical flow, though the alignment is weaker for clarity of explanation. These findings underscore the reliability of AI evaluations and suggest that they can serve as a complementary tool to traditional student feedback, offering objective, scalable insights into teaching quality. The study also highlights the limitations, such as reliance on transcribed text and the exclusion of non-verbal elements, indicating the need for multimodal AI models in future research. Finally, the paper suggests groundbreaking ideas for integrating AI into educational systems, with the potential to enhance teaching evaluation processes, making them more objective, accessible, and cost-effective, ultimately transforming the way teaching quality is assessed in academic institutions. |
| format | Article |
| id | doaj-art-195fe9a8d4174c498ae3d93404cf88cc |
| institution | DOAJ |
| issn | 2666-920X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers and Education: Artificial Intelligence |
| spelling | doaj-art-195fe9a8d4174c498ae3d93404cf88cc2025-08-20T02:47:19ZengElsevierComputers and Education: Artificial Intelligence2666-920X2025-12-01910044810.1016/j.caeai.2025.100448AI-based teaching evaluations: How well do they reflect student perceptions?Yossi Ben Zion0Shir Yakov1Einat Abramovitch2Gal Balter3Nitza Davidovitch4Department of Physics, Bar Ilan University, Ramat Gan 52900, Israel; Corresponding author.Department of Physics, Bar Ilan University, Ramat Gan 52900, IsraelDepartment of Physics, Bar Ilan University, Ramat Gan 52900, IsraelDepartment of Physics, Bar Ilan University, Ramat Gan 52900, IsraelDepartment of Education, Ariel University, Ariel 40700, IsraelThis study presents an innovative solution for evaluating university-level teaching quality using artificial intelligence (AI), focusing on key aspects such as clarity of explanation and lecture structure. Traditional student surveys, while valuable, are often subject to biases and lack the necessary granularity, creating a need for objective, scalable solutions that provide consistent results. We propose an automated framework utilizing advanced natural language processing (NLP) models to assess teaching quality based on lecture transcripts. The methodology combines AI-driven transcription, machine learning-based assessments, and correlation with institutional student evaluations to deliver reliable and reproducible measures of teaching effectiveness. The study analyzes 32 courses from 2017 to 2023, covering 1,222 hours of lecture video, and finds that AI assessments align significantly with student evaluations, particularly in terms of lecture structure and logical flow, though the alignment is weaker for clarity of explanation. These findings underscore the reliability of AI evaluations and suggest that they can serve as a complementary tool to traditional student feedback, offering objective, scalable insights into teaching quality. The study also highlights the limitations, such as reliance on transcribed text and the exclusion of non-verbal elements, indicating the need for multimodal AI models in future research. Finally, the paper suggests groundbreaking ideas for integrating AI into educational systems, with the potential to enhance teaching evaluation processes, making them more objective, accessible, and cost-effective, ultimately transforming the way teaching quality is assessed in academic institutions.http://www.sciencedirect.com/science/article/pii/S2666920X25000888AI-based course assessmentNatural language processing in educationAutomated teaching evaluationArtificial intelligence in educationTeaching quality assessment |
| spellingShingle | Yossi Ben Zion Shir Yakov Einat Abramovitch Gal Balter Nitza Davidovitch AI-based teaching evaluations: How well do they reflect student perceptions? Computers and Education: Artificial Intelligence AI-based course assessment Natural language processing in education Automated teaching evaluation Artificial intelligence in education Teaching quality assessment |
| title | AI-based teaching evaluations: How well do they reflect student perceptions? |
| title_full | AI-based teaching evaluations: How well do they reflect student perceptions? |
| title_fullStr | AI-based teaching evaluations: How well do they reflect student perceptions? |
| title_full_unstemmed | AI-based teaching evaluations: How well do they reflect student perceptions? |
| title_short | AI-based teaching evaluations: How well do they reflect student perceptions? |
| title_sort | ai based teaching evaluations how well do they reflect student perceptions |
| topic | AI-based course assessment Natural language processing in education Automated teaching evaluation Artificial intelligence in education Teaching quality assessment |
| url | http://www.sciencedirect.com/science/article/pii/S2666920X25000888 |
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