AI and Distance Ed

This week I would like to share a recent article exploring the relationship of AI on distance education (online learning). The article is entitled, “Exploring the impact of AI in enhancing the effectiveness of distance education: The moderating role of student engagement” by Sibarani (2025). The research explores how AI and student engagement can address distance learning concerns and enhance the effectiveness of online learning environments.

The study is grounded in two complementary theoretical frameworks:

Technology Acceptance Model (TAM) (Davis, 1989) explains how users adopt tech based on:

• Perceived usefulness -degree to which a person believes tech will enhance performance

• Perceived ease of use -degree to which a person believes using tech will be effortless

• Intention to use - likelihood that a person will use the tech

The author argues that for distance ed, the ease of operation of tech is fundamental—if platforms are difficult to use, teachers and students will be reluctant to engage with them. When technology is perceived as easy to use and beneficial, adoption rates increase.

Self-Determination Theory (SDT) (Ryan & Deci ,2017) examines motivation through the lens of three basic psychological needs:

• Autonomy - the need to feel in control of one's actions

• Competence - the need to master tasks and learn different skills

• Relatedness - the need to feel connected to others

In distance ed, SDT helps explain how student motivation is influenced by virtual social interactions and how this affects engagement with learning materials and activities.

The author proposes a three-paradigm model for AI in education:

1. Director: AI leads cognitive learning while learners are primarily recipients

2. Collaborator: AI supports learners who actively participate in the learning process

3. Empowering Tool: Learners take charge with AI facilitating their experience

The study considers AI applications, including ChatGPT, Perplexity, Consensus, Gemini, and other chatbots that provide personalized learning experiences.

Participants

• 281 respondents (127 male, 154 female) from various universities

• Educational: 66.2% undergraduate, 31.3% master's, 2.5% doctoral students

• Age: 50.9% aged 21-25, 34.9% under 20, 12.1% aged 26-30, 2.1% over 30

Participants completed surveys via Google Forms distributed through WhatsApp, Instagram, and email. The study used instruments adapted from previous research:

• AI usage instrument (Algaithi et al., 2024)

• Distance education instrument (Ozkaya et al., 2021)

• Student engagement instrument (Gunuc & Kuzu, 2015)

Data was analyzed using SmartPLS v.4 through bootstrapping and PLS algorithm techniques The model was validated using multiple criteria including factor loading, discriminant validity tests and multicollinearity assessment.

Hypothesis 1: AI Enhances Distance Ed Effectiveness

• Supported with statistical significance (t=1.731, p<0.10)

• AI tools provide immediate support to students when they encounter difficult material

• AI-powered tutors offer instant feedback and explanations of complex concepts

• Students who use AI consistently show improved productivity and performance

• AI makes information more accessible, serving as a support system for distance ed

Hypothesis 2: Student Engagement Enhances Distance Ed Effectiveness

• Strongly supported with statistical significance (t=5.382, p<0.05)

• Engaged students demonstrate higher motivation, focus, and participation in learning

• Student engagement increases learning motivation by making students feel more connected to materials

• Active engagement reinforces application of concepts through questions and discussion

• Social interaction increases with engagement, building a supportive learning community

• Engaged students develop better time management skills, essential for distance ed

Hypothesis 3: Student Engagement Strengthens the Impact of AI on Distance Ed

• Strongly supported with statistical significance (t=2.601, p<0.05)

• When actively engaged, the personalized learning offered by AI is fully optimized

• Engaged students are more likely to utilize AI-suggested materials

• High engagement strengthens the positive effects of AI-facilitated interactivity

• Fulfillment of autonomy and competence (from SDT) reinforces the positive impact of AI

Implications for this research suggests that educators should integrate AI tools that are useful and easy to use; develop strategies to actively promote student engagement; learning environments should be designed to fulfill students' needs for autonomy, competence, and relatedness; and synergy between AI and student engagement should be central to distance ed.

References

Sibarani, B. E. (2025). Exploring the impact of AI in enhancing the effectiveness of distance education: The moderating role of student engagement. Turkish Online Journal of Distance Education, 26(2), 133-148. https://doi.org/10.17718/tojde.1496906