AI evidence extraction

Personalization Imperative: Unpacking Student-AI Relationships Through a Mixed-Methods Lens in Indonesian Higher Education

Idha Novianti, Elang Krisnadi, Thesa Kandaga, Ranak Lince, Husnaeni Husnaeni, Nurmawati Nurmawati et al. · International Journal of Learning Teaching and Educational Research · 2026

AI-generated evidence extraction, verified across multiple analytical personas. Not a substitute for the peer-reviewed original.

5/10

Relevance

1/4

Quality (LMQS)

Evidence

Citations

0.00

FWCI

This is an AI analysis. Read the peer-reviewed original at the publisher: https://doi.org/10.26803/ijlter.25.1.35

Methodology & findings

Study design

Sequential exploratory mixed-methods design combining quantitative survey (n=85) with multiple regression analysis followed by qualitative thematic analysis of open-ended responses.

Sample

N = 85, 1 group

Primary method

Multiple regression analysis; bivariate correlation analysis; thematic analysis of qualitative data with percentage-based coding (e.g., 73.5%, 25.9%, 35.3%, 32.9%, 29.4%, 21.2%).

Main result

The multiple regression analysis revealed an intriguing paradox, indicating that "AI personalization is the only significant predictor of both satisfaction and intention to continue using AI tools (β = 0.450, p < 0.05), explaining 32.8% of variance." Despite showing a significant positive correlation in the bivariate analysis, AI effectiveness did not significantly predict satisfaction and continued intention. Qualitative analysis uncovered three superordinate themes including ambivalent experiences with 73.5% of students developing adaptive verification strategies, dual influences on collaboration where AI facilitated communication (25.9%) yet inhibited peer interaction (35.3%), and systemic challenges spanning epistemological (32.9%), pedagogical (29.4%), and social (21.2%) dimensions.

Reports effect sizes and confidence intervals.

Research paradigm

Mixed-methods (pragmatist/interpretivist-positivist)

Author conclusions

The authors conclude that "adaptive personalization is essential for converting the general effectiveness of AI into meaningful, context-specific value for learners, emphasizing that educational technologies must prioritize personalization to enhance engagement and support sustainable adoption in diverse learning settings." They further assert that "This study contributes to technology adoption theory by demonstrating that personalization acts as a key mediator, challenging traditional technology acceptance model assumptions."

Risk of bias

Selection bias: Single institution sample (Universitas Terbuka, Indonesia) may not be representative; Self-selection bias: Voluntary survey participation; Measurement bias: Self-reported satisfaction and adoption intentions; Confounding variables: Cultural, institutional, and individual factors not fully controlled; Selection bias: Single institution (Universitas Terbuka) may not represent diverse Indonesian higher education contexts; Sample composition: Mathematics education students only - field-specific effects possible; Self-selection bias: Voluntary survey participation from students with AI experience; Response bias: Open-ended responses may reflect more motivated or articulate respondents; Cross-sectional design: Cannot establish causality or temporal precedence; No control/comparison group: Lacks counterfactual for effectiveness claims; Selection bias: Single institution sample (Universitas Terbuka) may not represent broader Indonesian higher education; Self-selection bias: Survey respondents may differ systematically from non-respondents; Confounding variables: Student prior experience with technology, prior mathematical ability, and motivation not controlled; Response bias: Open-ended qualitative responses subject to social desirability bias

Open questions raised

The authors identify that limited attention has been paid to students' perspectives as primary users in culturally specific contexts such as developing countries, and that the mechanisms through which AI influences student satisfaction and sustained adoption remain insufficiently theorized, especially regarding the interplay between effectiveness and personalization.
The study identifies that 'limited attention has been paid to students' perspectives as primary users, particularly within culturally specific contexts such as developing countries' and that 'the mechanisms through which AI influences student satisfaction and sustained adoption remain insufficiently theorized, especially regarding the interplay between effectiveness and personalization.'
The study identifies the need for research on: (1) students' perspectives as primary users of AI, particularly in culturally specific contexts such as developing countries; (2) mechanisms through which AI influences student satisfaction and sustained adoption; (3) the interplay between effectiveness and personalization in AI-enhanced learning; (4) culturally responsive and effective educational technology design informed by empirical insights from diverse contexts like Indonesian higher education.

Data: not_statedCode: not_statedExtracted from: pdfAgreement 59%

Explore related topics

AI-Assisted Discovery Doctoral Training Mixed Methods