Personalized Feedback in Massive Open Online Courses: Harnessing the Power of LangChain and OpenAI API
Open accessAbstract
Studies show that feedback greatly improves student learning outcomes, but achieving this level of personalization at scale is a complex task, especially in the diverse and open environment of Massive Open Online Courses (MOOCs). This research provides a novel method for using cutting-edge artificial intelligence technology to enhance the feedback mechanism in MOOCs. The main goal of this research is to leverage AI’s capabilities to automate and refine the MOOC feedback process, with special emphasis on courses that allow students to learn at their own pace. The combination of LangChain—a cutting-edge framework specifically designed for applications that use language models—with the OpenAI API forms the basis of this work. This integration creates dynamic, scalable, and intelligent environments that can provide students with individualized, insightful feedback. A well-organized assessment rubric directs the feedback system, ensuring that the responses are both tailored to each learner’s unique path and aligned with academic standards and objectives. This initiative uses Generative AI to enhance MOOCs, making them more engaging, responsive, and successful for a diverse, international student body. Beyond mere automation, this technology has the potential to transform fundamentally how learning is supported in digital environments and how feedback is delivered. The initial results demonstrate increased learner satisfaction and progress, thereby validating the effectiveness of personalized feedback powered by AI.
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As of July 2026, ChatGPT Images 2.0 has no documented alt-text or accessibility feature.
“Product announcement emphasizes text-rendering accu- racy, speed, and multilingual support; no documented alt-text or accessibility feature.”
From A Protocol for Evaluating the Accessibility of AI-Generated Educational Materials: Prompt Configuration, WCAG-Derived Criteria, and Content Overload (2026) · cites this paper as [2]
The Introduction (p. 1) mentions DALL·E image creation, while the Discussion (p. 9) concerns ChatGPT feedback in MOOCs. Searches for ChatGPT Images 2.0, alt-text, accessibility, and the stated product-announcement features found no passage addressing whether that product had a documented accessibility feature as of July 2026. (LLM, conf=0.78)
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- Authors
- Morales-Chan, M., Amado-Salvatierra, H. R., Medina, J. A., Barchino, R., Hernández-Rizzardini, R., & Teixeira, A. M.
- Affiliations
- GES Department, Universidad Galileo, Guatemala City 01010, Guatemala
- Universidad de Alcalá (ES)
- Galileo University (GT)
- Department of Education and Distance Learning, Universidade Aberta de Portugal, 1250-100 Lisboa, Portugal
- ORCID
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2024-05-16
- Volume
- 13
- Issue
- 10
- Pages
- 1960
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4396952293
- Cited by
- 35
- References
- 24
- Citations by year
- 2026: 19 · 2025: 14 · 2024: 2
- FWCI
- 13.26
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- e_Buah (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- personalized feedback, massive open online courses, automated feedback, LangChain, OpenAI API, generative AI, assessment rubrics, self-paced learning, student satisfaction, artificial intelligence in education
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