Online Learning Inquiry

Our research/brainstorming process for is down below! Our group members: Alexis Moore, Mya Brar, Chanelle Rid, and Anam.

What is your distributed and open inquiry question? (For example, What is effective prompt engineering when using Generative AI tools? What is the timeline of progression of distributed learning tools through 2025? What are some best practices in terms of open educational practices with regard to inclusive learning?, etc.)

• Our question is: The Role of AI in Adaptive Learning: How personalized AI-driven content enhances online education. 

Timeline of progression of distributed learning with AI (Themed AI and education)

1. January 24 2025: UNESCO holds International day of education
   1. This was marked as the first coordinated recognition that AI will have a central role in developing and impacting education. From this point, there was a pivotal turn toward integrating AI literacy and AI enhanced educational tools (United Nations, 2025)
2. March 12 2025: Publication of systematic review of advancing tutoring through AI
   1. Research conducted by Vincent et al. (2025), indicated that there was enough empirical research on AI driven systems which resulted in the need for a global review. The research indicated that AI wasn’t speculative and that academic and institutional interest in AI centered educational tools for personalized and distributed learning was rising 
3. June 29, 2025: Publication ofAI and the future of education
   1. Niani (2025) explores how AI technology integrated education systems, such as ITS and NLP (Intelligent tutoring systems and Natural Language Processing), which were thought to improve education but had little evidence,  were academically validated. This was done on the assumption they have the potential to create personalized learning experiences (i.e individual feedback).
4. May, 31 2025: Publication of study AI in Education: Personalized Learning and Intelligent Tutoring Systems
   1. In her article, Yarlagadda (2025) emphasizes how AI is transforming  the face of education by effectively  creating personalized learning experiences.  However, she mentions that care should be given as to how it is implemented because even with these benefits, it raises concerns regarding data privacy and balancing technology vs. human influence in education 
5. June 2025-present: Microsoft “AI in Education report”
   1. The Microsoft report mentions that as of June 2025, policy makers and educational institutions have acknowledged that AI has penetrated the educational system, not as experimental tools but as a widely used support to create accessibility and diversity. 

What are some best practices in terms of open educational practices with regard to inclusive learning?

Some of the best practices of AI use in education include but are not limited to:

1. Ensuring ethics: This would require that educators learn and teach their students about ethical and transparent use of AI. Educators must ensure that any use of AI within the classroom remains transparent. This includes, for example, educating/showing students how AI systems can be used to generate feedback (Yarlagadda, 2025)
2. Pairing AI tools with other learning resources: Teachers should combine AI use with Open Educational Resources (OERs) to ensure accessibility. This especially becomes important to meet the learning needs of students who may not have access to AI and technology based sources (Yarlagadda, 2025)
3. Ensuring AI literacy: Teachers should educate students to access output from AI tools. Ensuring AI literacy also connects with AI ethics because with teaching AI literacy, teachers must ensure that students understand that AI can be learning partner but not a shortcut 

What is the relationship of your topic to teaching and learning?

Teaching:

• A 2023 report by the US Department of Education highlights that AI can tailor instructions to students’ needs, individual learning styles, and abilities (adaptive learning). 
• AI can make teaching easier by suggesting lesson plans and helping with grading. 
• When students see teachers are skilled with AI, they are more open to learning.
• Many concerns about integrating AI in an ethical, pedagogical way. 

AI has quickly become a popular tool for teachers, allowing them to plan lessons efficiently, grade and return work quickly, and allow them to spend more time focusing on creating relationships with their students. Research shows that by using AI responsibly and skillfully, student engagement and creativity can blossom. For example Zhou & Peng (2025) demonstrate how AI can foster creativity in students and teachers. They focus on the conservation of resource theory emphasizing that when teachers use AI they have more time and energy to invest with the students. These “saved resources” can be used to foster positive relationships with the students, which intrinsically encourages students to engage more through creativity. 

 However, if instructors rely too heavily on AI, this can in turn weaken teacher-student relationships and create a lack of confidence for both students and instructors. For example, according to Gray et al., 2025, when AI is overused, students may question an instructor’s academic integrity. AI also poses privacy and security risks.

Learning:

• Allows for adaptive, personalized learning
• Real time feedback
• Adds value by making learning more accessible
• Some argue can reduce independent problem solving and critical thinking
• Reduce motivation
• Academic integrity concerns
• Dependence on technology

AI has transformed learning by allowing students to tailor their learning to their unique needs, and receive real-time feedback. AI has made learning instant and more accessible. For example, Letourneau et al. emphasize ITS provide immediate one on one instruction and feedback that supports individualized learning. They found that the one-on-one learning environment AI provides is hard to match in traditional classrooms. 

It must also be noted that AI has had a severe impact on learner’s critical thinking skills and ability to make independent decisions. For example, in a systematic review, Melissa et al. found that while AI can scaffold higher-level thinking skills, studies indicate that overuse or reliance correlates with poorer critical thinking skills. AI can reduce motivation and social interaction. There are also major concerns about academic integrity violations and plagiarism. AI is a powerful, useful learning tool, however, overuse can lead to technology dependence and a lack in cognitive development. 

What are the pros, cons, and risks?

PROS 

Personalized Learning Journeys — According to Liue et al. (2025) AI integrated education systems, such as ITS (including MATHia/ACTIVE Math) can help to create personalized pathways and encourage student engagement. This is because these tools AI adapts content to each learner’s pace, style, and prior knowledge. For example, intelligent tutoring systems like MATHia or ACTIVE Math adjust problem difficulty in real time, ensuring students are challenged but not overwhelmed. This personalization fosters motivation and retention.

Efficiency in Administration — In a systematic review by Kumar (2024), it was found that AI tools such as automated grading systems, plagiarism detection, and scheduling can facilitate instructors by reducing instructor workload. This reinforces Zhou & Peng (2025) idea of conservation of resources as it frees educators to focus on higher-order teaching tasks such as mentoring, discussion facilitation, and curriculum innovation.

Global Accessibility — AI-powered translation tools and web-based platforms break down language and geographic barriers. Emphasizing this idea, United Nations (2025) highlight that AI translation systems can increase global accessibility that would help to remove language and geographic barriers. In this way, learners in different countries can access the same materials, participate in virtual classrooms, and collaborate across borders.

Immersive and Experiential Learning —  Virtual reality and 3D simulations allow students to experience complex concepts—like anatomy or engineering—through interactive environments. This experiential learning deepens understanding beyond traditional lectures. Medical students can use it to overlay onto subjects to learn anatomy 

Real-Time Analytics —  AI dashboards provide instructors with insights into student progress, flagging learners at risk and enabling timely interventions.

CONS 

Digital Fatigue and Cognitive Overload —  According to Ibrahim et al. (2025), their research indicated that the use of multiple educational technologies was significantly associated with increased cognitive load which correlated with psychological well-being and fatigue. Constant engagement with AI-driven platforms can exhaust learners. Notifications, adaptive quizzes, and continuous monitoring may overwhelm students, reducing intrinsic motivation.

Uneven Participation in Group Work. — After conducting a systematic interview, Zhai & Li (2024) found that AI has the potential of limiting student interactions. While AI can personalize individual learning, it struggles to balance group dynamics. Some students may dominate collaborative tasks while others disengage, leading to inequitable outcomes.

Dependence on Technology —  Students may rely too heavily on AI rather than developing independent problem-solving skills. Technical issues can disrupt learning.

Reduced Human Interaction –  Zhai & Li (2024), also in their review,  found that automated feedback results in less student-teacher interaction This may lead to weakened relationships that would severely impact the student’s perception on the impact of his/her educator in their educational journey. AI cannot replace the emotional support, mentorship, and nuance of human educators. Risk of more isolated learning environments.

Costs and Accessibility Barriers – High-quality adaptive AI systems can be expensive for institutions. Low-income learners may lack the technology needed to access advanced tools.

RISKS: 

Privacy and Surveillance AI platforms often collect sensitive data, learning behaviors, emotional responses, even biometric information in VR. Without strict safeguards, this data can be misused or exploited.

Algorithmic Bias and Inequity —  AI systems trained on biased datasets may reinforce stereotypes. For example, predictive analytics could mislabel students from marginalized groups as “low-performing,” perpetuating systemic inequities.

Transparency and Accountability — Many AI systems operate as “black boxes,” making it difficult for educators to understand how decisions are made. Lack of transparency undermines trust and accountability.

Depersonalization of Learning — If AI replaces too many instructor functions, students may lose opportunities for mentorship, dialogue, and relational learning, elements essential for holistic education.

Ethical Concerns in Automation — Delegating grading, assessment, or even teaching to AI raises questions about fairness, responsibility, and the role of educators. Who is accountable if an AI system misjudges a student’s performance?

Long-Term Societal Impact As AI permeates education — it may reshape labor markets, skill requirements, and even the definition of “learning.” Students trained in highly adaptive systems may struggle in environments without AI support.

What are some of the strategies, best practices, and tips regarding your inquiry findings? (alexis) 

When looking at the research on AI-focused learning, several strategies emerge. 

1. Have clear learning goals 

• AI is most effective when teachers define what students need to learn. 
• After the students know what their goal is, they can use AI to give extra practice and personalize activities for the best way they retain information. 
• (du Plooy et al., 2024) found that improved performance only occurred when paired with intentional goal setting 

2. Use AI to help support teachers, not replace them

• Teachers can use AI’s suggestions, but ultimately, they know best what works for their students, so building on them while tailoring them to their students’ needs is crucial. 
• The OECD (2023) and the U.S. Department of Education (2023) both emphasize including humans with the use of AI as the best practice for accuracy, as well as safety

3. Be transparent with learners 

• Being transparent with your students about the integration of AI allows them to understand the positives and negatives, and they can use AI to help support their personal learning 
• It is important to be transparent to protect student privacy as well, and remind students to only give information that is needed 

4. Check AI for biases that can occur 

• AI often has a bias and can give you information based on information that you have previously given AI.
• This creates disadvantages as it gives you biases, so checking AI’s work for an unbiased opinion is crucial 

References 

Abrams, Z. (2025). Trends in classrooms: Artificial intelligence. APA Monitor on Psychology. https://www.apa.org/monitor/2025/01/trends-classrooms-artificial-intelligence

Du Plooy, E et al., (2024). Personalized adaptive learning in higher education: A scoping review of key characteristics and impact on academic performance and engagement. Heliyon, https://doi.org/10.1016/j.heliyon.2024.e39630

Eastgate, J. (2024). 10 potential negative effects of AI in education. Medium. https://medium.com/@eastgate/10-potential-negative-effects-of-ai-in-education-151634ef3b54

Giarla, A. (n.d.). The negative effects of artificial intelligence in education. StratX Simulations. https://web.stratxsimulations.com/recent-posts/the-negative-effects-of-artificial-intelligence-in-education

Ibrahim, R. K. et al., (2025). Impact of multiple educational technologies on well-being: the mediating role of digital cognitive load. BMC Nursing, 24(1), Article 1028. https://doi.org/10.1186/s12912-025-03655-z

Kumar, R. (2024). Faculty members’ use of artificial intelligence to grade student papers: a case of implications. International Journal for Educational Integrity, 19(1), Article 9. https://doi.org/10.1007/s40979-023-00130-7

Melo-López, V.-A., et al (2025). The Impact of Artificial Intelligence on Inclusive Education: A Systematic Review. Education Sciences, 15(5), 539. https://doi.org/10.3390/educsci15050539

Nagelhout, R. (2025). AI can add — not just subtract — learning. Harvard Graduate School of Education. https://www.gse.harvard.edu/ideas/news/25/04/ai-can-add-not-just-subtract-learning

OECD. (2023). OECD Digital Education Outlook 2023: Towards an Effective Digital Education Ecosystem. OECD Publishing. https://doi.org/10.1787/c74f03de-en

U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report.pdf

United Nations Educational, Scientific and Cultural Organization. (2023). Guidance for generative AI in education and research. UNESCO Publishing. https://doi.org/10.54675/EWZM9535 Zhai, C et al., (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: a systematic review. Smart Learning Environments, 11(1), Article 28. https://doi.org/10.1186/s40561-024-00316-7