> How LMS-Based Courses Can Be Enhanced by AI GPTs

By Walter Rodriguez, PhD, PE

How LMS-Based Courses Can Be Enhanced by AI GPTs

Abstract

Learning Management Systems (LMSs) have become central to the delivery of online education across K–12, vocational and trade, higher education, and corporate certification training. While LMS platforms provide infrastructure for content delivery, scheduling, and assessment, they often lack the adaptability and personalization associated with human tutors. The emergence of Generative Pre-trained Transformers (GPTs)—huge language models (LLMs) such as OpenAI’s ChatGPT—offers a transformative opportunity to enhance LMS-based learning experiences by enabling interactive, intelligent, and adaptive educational support.

Personalized and Adaptive Learning

One of the key limitations of traditional Learning Management Systems (LMSs) is their static nature of content delivery. GPT-based AI tools can dynamically adapt instructions to individual learner needs by analyzing user inputs and responding with tailored explanations, examples, and feedback (Zawacki-Richter et al., 2019). This allows learners to receive just-in-time guidance that closely mimics one-on-one tutoring, an instructional model known to be highly effective (Bloom, 1984). For instance, a student struggling with a statistics problem within a course on Canvas can prompt a GPT to walk them through the solution, using scaffolding techniques aligned with Vygotsky’s zone of proximal development (Luckin et al., 2016).

Intelligent Tutoring and Feedback

AI GPTs can also serve as intelligent tutoring systems embedded within Learning Management System (LMS) modules. Unlike pre-programmed chatbots, GPTs understand nuanced learner queries and generate context-specific responses. This functionality enables real-time Q&A, correction of misconceptions, and elaboration on complex topics (Holmes et al., 2019). Moreover, GPTs can provide formative feedback on student writing, discussion forum posts, and coding assignments, enhancing the feedback loop that is often limited in instructor-led online courses.

Content Creation and Course Design Support

Instructors can use GPTs to assist with course design by generating quiz questions, case studies, summaries, rubrics, and even multimedia scripts (Baidoo-Anu & Owusu Ansah, 2023). This capability reduces instructional workload, allowing faculty to focus more on pedagogy than content generation. Furthermore, AI-generated content can be aligned with Bloom’s taxonomy or Universal Design for Learning (UDL) principles to ensure cognitive progression and accessibility.

Enhanced Engagement Through Conversational Learning

Conversational interfaces powered by GPTs promote learner engagement by supporting natural language interactions. This aligns with theories such as Krashen’s Input Hypothesis and Bandura’s Social Learning Theory, suggesting that language and knowledge are acquired more effectively in meaningful, low-stress environments (Krashen, 1982; Bandura, 1977). Integrating GPTs into LMS-based courses enables learners to explore “what-if” scenarios, engage in simulations, and practice language or reasoning skills in a conversational format, thereby improving both cognitive and affective learning outcomes.

Limitations and Ethical Considerations

Despite the promise of GPTs, challenges remain. Current models may produce inaccurate information or reflect biases inherent in training data. Ensuring alignment with academic integrity standards, particularly in assessment, is crucial (Flanagan & Wilson, 2023). Moreover, LMS-GPT integration must be transparent and designed to protect student data privacy, as mandated by laws like FERPA and GDPR. (Please see the Appendix below for addressing those issues.)

Future Directions

Ongoing research and development aim to fine-tune GPTs for specific educational domains and integrate them natively into Learning Management System (LMS) environments, such as Moodle, Canvas, and Blackboard. Innovations such as AI Teaching Assistants (AITAs) or course-specific GPTs trained on proprietary content are emerging, signaling a shift toward AI-personalized learning ecosystems (Chiu et al., 2023).

Conclusion

The integration of GPT-powered AI into Learning Management System (LMS)-based courses represents a significant shift in digital education. By enabling adaptive learning, intelligent tutoring, automated content support, and conversational interaction, GPTs significantly enhance the capabilities of traditional Learning Management System (LMS) platforms. However, responsible implementation, ongoing evaluation, and ethical vigilance are essential to ensure that these powerful tools serve all learners equitably and effectively.

References

Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative AI: Understanding and leveraging ChatGPT for teaching and learning. Education and Information Technologies, 28(4), 5075–5096. https://doi.org/10.1007/s10639-023-11608-w

Bandura, A. (1977). Social learning theory. Prentice Hall.

Bloom, B. S. (1984). The 2-sigma problem: The search for methods of group instruction as effective as one-to-one tutoring. Educational Researcher, 13(6), 4–16. https://doi.org/10.3102/0013189X013006004

Chiu, T. K. F., Lin, T. J., & Lonka, K. (2023). AI teaching assistants: Conceptual frameworks and design implications for learning analytics. British Journal of Educational Technology, 54(1), 18–34. https://doi.org/10.1111/bjet.13283

Flanagan, B., & Wilson, D. (2023). ChatGPT and the academic integrity dilemma: Implications for assessment design. Assessment & Evaluation in Higher Education, 48(4), 579–594. https://doi.org/10.1080/02602938.2023.2193919

Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.

Krashen, S. D. (1982). Principles and practice in second language acquisition. Pergamon Press.

Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education. https://doi.org/10.5281/zenodo.1481108

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education: A bibliometric analysis. International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0

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Appendix

Addressing the limitations and ethical considerations of integrating GPTs into LMS-based courses is critical to ensuring the responsible, inclusive, and effective use of these tools. Below are strategic suggestions, organized by issue:

Inaccuracy and Hallucination

Problem: GPTs can generate plausible but incorrect or misleading information.

Suggestions:

• Human-in-the-loop design: Requires human review or moderation for critical feedback, especially in assessments or content creation.

• Model fine-tuning and grounding: Train custom GPTs on verified course materials or integrate retrieval-based architectures to ground responses in official LMS content (e.g., lecture notes, textbooks, policies).

• Prompt engineering templates: Standardize prompts used by learners to reduce the risk of misinterpretation or off-topic responses.

Bias and Cultural Insensitivity

Problem: GPTs may reflect and reproduce biases inherent in their training data, potentially affecting fairness and inclusivity.

Suggestions:

• Bias audits and testing: Regularly evaluate AI outputs for fairness using diverse learner scenarios.

• Inclusive prompt design: Craft culturally sensitive prompts and role-play scenarios within LMS activities that represent diverse viewpoints.

• Customization for local context: Fine-tune models with datasets reflective of the learner population's linguistic, cultural, and pedagogical context.

Data Privacy and Surveillance

Problem: Use of AI systems may compromise FERPA, GDPR, or institutional privacy policies.

Suggestions:

• Local hosting or privacy-compliant APIs: Use GPT instances via platforms that guarantee data security (e.g., OpenAI’s EDU API, Azure OpenAI, or private LLMs like Mistral or Claude hosted on secure servers).

• Transparent data policies: Inform students about what data is collected, how it's used, and obtain opt-in consent.

• Minimize identifiable data sharing: Avoid feeding student grades, names, or sensitive submissions into public LLMs.

Academic Integrity and Over-Reliance

Problem: Learners may use GPTs to complete assignments dishonestly, or over-rely on AI to the detriment of critical thinking.

Suggestions:

• Redesign assessments: Shift toward open-ended, process-focused, or collaborative tasks that require human insight and reflection.

• Use GPTs to teach metacognition: Create assignments that require students to compare their response to a GPT and critique it.

• AI usage guidelines: Include a "Responsible Use of AI" section in course syllabi and LMS policy modules.

Digital Divide and Access

Problem: Not all students have equal access to AI tools or possess the digital literacy to use them effectively.

Suggestions:

• Equity-focused implementation: Provide institutional access to GPTs within the LMS so all students benefit equally, regardless of personal subscriptions or devices.

• Onboarding support: Offer tutorials or workshops on using GPTs constructively for learning, rather than just for obtaining answers.

• Scaffolded introduction: Introduce GPT-based tools gradually, paired with instructor guidance and peer support communities.