The Integration Technology-driven Learning

Diane Gavin, Ph.D., Executive Director, Center for Academic Innovation, Texas A&M University, San Antonio

The field of technology has offered groundbreaking inventions in the last decade, some modified and others new. Academic institutions are pacing with these innovations to enhance the learning and teaching experience for individuals. Taking this objective ahead is Diane Gavin, who discusses the benefits of integrated classrooms and learning systems in this article. She highlights the need for data privacy and safety in this quickly evolving digitalized atmosphere.

The field of technology has offered groundbreaking inventions in the last decade, some modified and others new. Academic institutions are pacing with these innovations to enhance the learning and teaching experience for individuals. Taking this objective ahead is Diane Gavin, who discusses the benefits of integrated classrooms and learning systems in this article. She highlights the need for data privacy and safety in this quickly evolving digitalized atmosphere.

Artificial Intelligence (AI), an effective facilitator

Education technologists working with Learning Management Systems (LMS) know that AI technology has been or is being integrated into LMS systems. Regardless of the LMS used on a college or university campus, integrating AI-powered features within the LMS claims increased student engagement, course efficiency and instructional effectiveness. LMS marketing strategies all seem to suggest AI will reduce faculty’s administrative burdens and will offer data-informed insights to improve course design, course outcomes, and delivery.

The "AI promise" for LMS systems sounds like a dream for some educational technologists and faculty. Can the dream be a reality? AI integration in LMS appears to provide students with personalized learning pathways and tailored content for specific students’ needs. For faculty, AI may provide instructors with automated grading and feedback, predictive analytics, adaptive assessments and intelligent tutoring systems to students.

“While there are some in higher education that have an enduring sense of society’s overreliance upon the use of technology and the loss of humanity in teaching, AI-driven technology is not going away.”

The catch? Thinking differently about how we and our faculty engage in course design, delivery, assessment and AI. While some in higher education have an enduring sense of society’s overreliance on technology and the loss of humanity in teaching, AI-driven technology is not going away. Akin to a genie, it was let out of the bottle starting in the 1950s and entered the education arena decades ago in the early 1960s with the use of Eliza, a computer tutorial software program. Going back to an idealized past is not an option.

Noted and central concerns about integrating AI into LMS

1.  Data privacy, security, and ethics. Yes, LMS systems collect and store large amounts of student data and potentially be subjected to data breaches or misuse. However, higher education institutions’ LMS/ IT plans need compliance with The Family Educational Rights and Privacy Act (FERPA) in the U.S. and the General Data Protection Regulation (GDPR) in Europe; those institutions with international programs follow FERPA and GDPR. Check the LMS system to learn whether FERPA and GDPR are used to protect student data privacy.

2.  Monitoring students: Some faculty members may have concerns about AI for monitoring students or balancing data personalization with student autonomy. However, there are multiple tracking systems used on college and university campuses to watch students for test proctoring and predictive advising for the last 20 years. Similarly, data personalization and student autonomy in higher education are covered under FERPA and GDPR guidelines.

3.  Biases: Algorithmic biases have been reported in AI systems that could perpetuate or amplify existing biases in higher education. Such algorithmic biases run the risk of unfair treatment or assigning opportunities based on demographic factors.

4.  Transparent explanations of AI determinations: Some algorithmic patterns or activities have been difficult to explain (e.g., “the AI black box”) to non-technologically driven users. Limited knowledge of the process of AI determinations or recommendations generates difficulty for some users in trusting the AI components’ reliability and validity.

5.  Equity: The digital divide is once again real; however, the accessibility and equity issues include the need for learners with disabilities to have fair access to learning. Adopt an LMS that has accessibility technology for fairness in student access.

Do remember

When implementing AI technology in an LMS system, focus on whether the AI models offer transparency and clear documentation about the AI system’s purpose, data sources, decision-making criteria and storage of student data. Conducting regular independent audits of the AI programs and courses to check for biases, errors or unexpected behaviors is also important; reach out to faculty and see if these problems are occurring. If so, address them at once. Establish the synergy of LMS technologies and faculty, allowing humans to initiate the intervention of AI appropriately.

Additionally, train faculty and students on AI as a decision-making option within the LMS to encourage understanding and trust of artificial intelligence as used in a course. Lastly, faculty, students and educational technologists should work together to develop and follow clear ethical guidelines for AI use in courses; specific courses may require specific guidelines. A one-size-fits-all model may not be right for everyone. Stakeholder education and training are central to smoother AI integration in LMS systems, regardless of which LMS is used.

Artificial Intelligence

Storage

Predictive Analytics

Learning Management

Test

Marketing