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A Comparative Study of AI-Generated and Human-Crafted Learning Objectives in Computing Education

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Title: A Comparative Study of AI-Generated and Human-Crafted Learning Objectives in Computing Education
Language: English
Authors: Aidan Doyle, Pragnya Sridhar, Arav Agarwal, Jaromir Savelka (ORCID 0000-0002-3674-5456), Majd Sakr
Source: Journal of Computer Assisted Learning. 2025 41(1).
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 16
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Descriptors: Computer Science Education, Artificial Intelligence, Learning Objectives, Curriculum Development, Relevance (Education), Curriculum Design, Computer Assisted Design, Comparative Testing, Teacher Developed Materials, Course Objectives, Course Organization
DOI: 10.1111/jcal.13092
ISSN: 0266-4909
1365-2729
Abstract: Background: In computing education, educators are constantly faced with the challenge of developing new curricula, including learning objectives (LOs), while ensuring that existing courses remain relevant. Large language models (LLMs) were shown to successfully generate a wide spectrum of natural language artefacts in computing education. Objectives: The objective of this study is to evaluate if it is feasible for a state-of-the-art LLM to support curricular design by proposing lists of high-quality LOs. Methods: We propose a simple LLM-powered framework for the automatic generation of LOs. Two human evaluators compare the automatically generated LOs to the human-crafted ones in terms of their alignment with course goals, meeting the SMART criteria, mutual overlap, and appropriateness of ordering. Results: We found that automatically generated LOs are comparable to LOs authored by instructors in many respects, including being measurable and relevant while exhibiting some limitations (e.g., sometimes not being specific or achievable). LOs were also comparable in their alignment with the high-level course goals. Finally, auto-generated LOs were often deemed to be better organised (order, non-overlap) than the human-authored ones. Conclusions: Our findings suggest that LLM could support educators in designing their courses by providing reasonable suggestions for LOs.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1459005
Database: ERIC
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  Data: A Comparative Study of AI-Generated and Human-Crafted Learning Objectives in Computing Education
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  Data: <searchLink fieldCode="AR" term="%22Aidan+Doyle%22">Aidan Doyle</searchLink><br /><searchLink fieldCode="AR" term="%22Pragnya+Sridhar%22">Pragnya Sridhar</searchLink><br /><searchLink fieldCode="AR" term="%22Arav+Agarwal%22">Arav Agarwal</searchLink><br /><searchLink fieldCode="AR" term="%22Jaromir+Savelka%22">Jaromir Savelka</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-3674-5456">0000-0002-3674-5456</externalLink>)<br /><searchLink fieldCode="AR" term="%22Majd+Sakr%22">Majd Sakr</searchLink>
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  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
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  Data: 16
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  Data: 10.1111/jcal.13092
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  Data: 0266-4909<br />1365-2729
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Background: In computing education, educators are constantly faced with the challenge of developing new curricula, including learning objectives (LOs), while ensuring that existing courses remain relevant. Large language models (LLMs) were shown to successfully generate a wide spectrum of natural language artefacts in computing education. Objectives: The objective of this study is to evaluate if it is feasible for a state-of-the-art LLM to support curricular design by proposing lists of high-quality LOs. Methods: We propose a simple LLM-powered framework for the automatic generation of LOs. Two human evaluators compare the automatically generated LOs to the human-crafted ones in terms of their alignment with course goals, meeting the SMART criteria, mutual overlap, and appropriateness of ordering. Results: We found that automatically generated LOs are comparable to LOs authored by instructors in many respects, including being measurable and relevant while exhibiting some limitations (e.g., sometimes not being specific or achievable). LOs were also comparable in their alignment with the high-level course goals. Finally, auto-generated LOs were often deemed to be better organised (order, non-overlap) than the human-authored ones. Conclusions: Our findings suggest that LLM could support educators in designing their courses by providing reasonable suggestions for LOs.
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      – Text: English
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        PageCount: 16
    Subjects:
      – SubjectFull: Computer Science Education
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Learning Objectives
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      – SubjectFull: Teacher Developed Materials
        Type: general
      – SubjectFull: Course Objectives
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      – SubjectFull: Course Organization
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      – TitleFull: A Comparative Study of AI-Generated and Human-Crafted Learning Objectives in Computing Education
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