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AI-Assisted Co-Creation: Bridging Skill Gaps in Student-Generated Content

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Title: AI-Assisted Co-Creation: Bridging Skill Gaps in Student-Generated Content
Language: English
Authors: Stanislav Pozdniakov (ORCID 0000-0003-4451-9181), Jonathan Brazil (ORCID 0000-0002-6669-2076), Mehrnoush Mohammadi (ORCID 0000-0001-9596-7414), Mollie Dollinger (ORCID 0000-0003-1105-9051), Shazia Sadiq (ORCID 0000-0001-6739-4145), Hassan Khosravi (ORCID 0000-0001-8664-6117)
Source: Journal of Learning Analytics. 2025 12(1):129-151.
Availability: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
Peer Reviewed: Y
Page Count: 29
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Student Evaluation, Artificial Intelligence, Student Developed Materials, Feedback (Response), Multiple Choice Tests, Foreign Countries, College Students, Learning Analytics, Student Attitudes, Technology Uses in Education, Educational Technology
Geographic Terms: Australia
ISSN: 1929-7750
Abstract: Engaging students in creating high-quality novel content, such as educational resources, promotes deep and higher-order learning. However, students often lack the necessary training or knowledge to produce such content. To address this gap, this paper explores the potential of incorporating generative AI (GenAI) to review students' work and provide them with real-time feedback and assistance during content creation. Specifically, we use RiPPLE, which enables students to create bite-size learning resources and incorporates instant GenAI feedback, highlighting strengths and suggesting improvements to enhance quality. The AI reviews the resource and provides feedback encompassing three main components: a summary of the resource, a list of strengths, and suggestions for improvement. We evaluate this approach by analyzing log data from 1063 student-created multiple-choice questions (MCQs) and the corresponding AI feedback. This analysis aims to understand the depth, scope, and tone of the feedback provided by the AI, as well as the way students engage with and utilize this feedback in their content creation process. Additionally, we examined the perceived helpfulness of the GenAI feedback analyzed via 3324 student ratings and thematically analyzed 601 comments they provided about the feedback. Our findings demonstrate the potential value of AI-generated feedback for students when integrated into pedagogical design. Our analysis suggests that not only can AI-generated feedback provide students with a breadth of feedback to improve their writing and/or discipline-specific content knowledge, but also it is largely well received by students for both its clarity and its positive tone. Despite challenges in ensuring the accuracy of AI-generated feedback, this study shows how this feedback can enable students to make actionable changes in their academic performance.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1465725
Database: ERIC
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  Data: AI-Assisted Co-Creation: Bridging Skill Gaps in Student-Generated Content
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  Data: <searchLink fieldCode="AR" term="%22Stanislav+Pozdniakov%22">Stanislav Pozdniakov</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4451-9181">0000-0003-4451-9181</externalLink>)<br /><searchLink fieldCode="AR" term="%22Jonathan+Brazil%22">Jonathan Brazil</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6669-2076">0000-0002-6669-2076</externalLink>)<br /><searchLink fieldCode="AR" term="%22Mehrnoush+Mohammadi%22">Mehrnoush Mohammadi</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-9596-7414">0000-0001-9596-7414</externalLink>)<br /><searchLink fieldCode="AR" term="%22Mollie+Dollinger%22">Mollie Dollinger</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-1105-9051">0000-0003-1105-9051</externalLink>)<br /><searchLink fieldCode="AR" term="%22Shazia+Sadiq%22">Shazia Sadiq</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-6739-4145">0000-0001-6739-4145</externalLink>)<br /><searchLink fieldCode="AR" term="%22Hassan+Khosravi%22">Hassan Khosravi</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0001-8664-6117">0000-0001-8664-6117</externalLink>)
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  Data: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
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  Data: Engaging students in creating high-quality novel content, such as educational resources, promotes deep and higher-order learning. However, students often lack the necessary training or knowledge to produce such content. To address this gap, this paper explores the potential of incorporating generative AI (GenAI) to review students' work and provide them with real-time feedback and assistance during content creation. Specifically, we use RiPPLE, which enables students to create bite-size learning resources and incorporates instant GenAI feedback, highlighting strengths and suggesting improvements to enhance quality. The AI reviews the resource and provides feedback encompassing three main components: a summary of the resource, a list of strengths, and suggestions for improvement. We evaluate this approach by analyzing log data from 1063 student-created multiple-choice questions (MCQs) and the corresponding AI feedback. This analysis aims to understand the depth, scope, and tone of the feedback provided by the AI, as well as the way students engage with and utilize this feedback in their content creation process. Additionally, we examined the perceived helpfulness of the GenAI feedback analyzed via 3324 student ratings and thematically analyzed 601 comments they provided about the feedback. Our findings demonstrate the potential value of AI-generated feedback for students when integrated into pedagogical design. Our analysis suggests that not only can AI-generated feedback provide students with a breadth of feedback to improve their writing and/or discipline-specific content knowledge, but also it is largely well received by students for both its clarity and its positive tone. Despite challenges in ensuring the accuracy of AI-generated feedback, this study shows how this feedback can enable students to make actionable changes in their academic performance.
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RecordInfo BibRecord:
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    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 29
        StartPage: 129
    Subjects:
      – SubjectFull: Student Evaluation
        Type: general
      – SubjectFull: Artificial Intelligence
        Type: general
      – SubjectFull: Student Developed Materials
        Type: general
      – SubjectFull: Feedback (Response)
        Type: general
      – SubjectFull: Multiple Choice Tests
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      – SubjectFull: Foreign Countries
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      – SubjectFull: College Students
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      – SubjectFull: Learning Analytics
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      – SubjectFull: Student Attitudes
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      – SubjectFull: Technology Uses in Education
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      – SubjectFull: Educational Technology
        Type: general
      – SubjectFull: Australia
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    Titles:
      – TitleFull: AI-Assisted Co-Creation: Bridging Skill Gaps in Student-Generated Content
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              Y: 2025
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