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September 25, 2025
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October 13, 2025
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October 14, 2025
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Abstract

Artificial intelligence (AI) has become a disruptive force in education in the digital age, with the potential to increase student motivation, engagement, and curiosity. Although efficiency, evaluation, and material delivery are frequently highlighted in current AI applications, its potential to promote intrinsic motivation and deeper learning is still poorly understood. Guided by Self-Efficacy Theory, Expectancy-Value Theory, and Self-Determination Theory, this study systematically synthesizes recent literature to explore the relationship between AI and student motivation. Using a systematic literature synthesis approach, relevant peer-reviewed articles from 2010 to 2024 were analyzed to identify how AI tools such as gamification, adaptive learning platforms, intelligent tutoring systems, and emotion-aware technologies influence motivational constructs including task value, autonomy, competence, relatedness, and expectancy beliefs. The results indicate that AI solutions can support critical motivational drivers by creating adaptive, emotionally responsive, and learner-centered environments. Specifically, AI fosters curiosity and engagement by providing immediate feedback, personalized pathways, and psychologically meaningful learning experiences. However, the study also identifies challenges, such as limited inclusivity for diverse learners, risks of over-automation, data privacy concerns, and ethical dilemmas in algorithmic decision-making. These limitations highlight the importance of aligning AI development with human-centered pedagogical principles. Practical implications suggest that while developers and policymakers must prioritize transparency, diversity, and ethical design, educators can leverage AI to tailor instruction, maintain curiosity, and foster emotionally supportive learning settings. By reframing AI not merely as a performance optimization tool but as a motivational partner that cultivates lifelong curiosity and meaningful learning engagement, this study contributes to the growing body of research on human-centered AI in education. It also proposes a conceptual framework that bridges cognitive, motivational, and emotional dimensions of learning, positioning AI as an active collaborator in fostering curiosity and sustainable motivation.


[Kecerdasan buatan (AI) telah menjadi kekuatan disruptif dalam dunia pendidikan di era digital, dengan potensi besar untuk meningkatkan motivasi, keterlibatan, dan rasa ingin tahu peserta didik. Meskipun berbagai aplikasi AI saat ini banyak menyoroti efisiensi, evaluasi, dan penyampaian materi, potensi AI dalam mendorong motivasi intrinsik dan pembelajaran yang lebih mendalam masih belum sepenuhnya dipahami. Berlandaskan Self-Efficacy Theory, Expectancy-Value Theory, dan Self-Determination Theory, penelitian ini secara sistematis mensintesis literatur terkini untuk menelusuri hubungan antara AI dan motivasi belajar siswa. Melalui pendekatan systematic literature synthesis, artikel-artikel ilmiah terverifikasi dari tahun 2010 hingga 2024 dianalisis untuk mengidentifikasi bagaimana alat-alat AI seperti gamifikasi, platform pembelajaran adaptif, sistem tutor cerdas, dan teknologi yang peka terhadap emosi memengaruhi konstruksi motivasional, termasuk nilai tugas (task value), otonomi, kompetensi, keterhubungan (relatedness), dan keyakinan ekspektasi (expectancy beliefs). Hasil penelitian menunjukkan bahwa solusi berbasis AI dapat mendukung faktor-faktor penggerak motivasi utama dengan menciptakan lingkungan belajar yang adaptif, responsif secara emosional, dan berpusat pada peserta didik. Secara khusus, AI mampu menumbuhkan rasa ingin tahu dan keterlibatan dengan memberikan umpan balik langsung, jalur pembelajaran yang dipersonalisasi, serta pengalaman belajar yang bermakna secara psikologis. Namun demikian, penelitian ini juga mengidentifikasi beberapa tantangan, seperti keterbatasan inklusivitas bagi peserta didik yang beragam, risiko otomatisasi berlebihan, isu privasi data, dan dilema etis dalam pengambilan keputusan algoritmik. Berbagai keterbatasan tersebut menegaskan pentingnya menyelaraskan pengembangan AI dengan prinsip pedagogi yang berpusat pada manusia. Implikasi praktis menunjukkan bahwa pengembang dan pembuat kebijakan perlu memprioritaskan transparansi, keberagaman, dan desain etis, sementara para pendidik dapat memanfaatkan AI untuk menyesuaikan pembelajaran, mempertahankan rasa ingin tahu, dan membangun lingkungan belajar yang mendukung secara emosional. Dengan memosisikan kembali AI bukan semata sebagai alat optimasi kinerja, melainkan sebagai mitra motivasional yang menumbuhkan rasa ingin tahu sepanjang hayat dan keterlibatan belajar yang bermakna, penelitian ini memberikan kontribusi bagi pengembangan wacana tentang AI berorientasi kemanusiaan dalam pendidikan. Penelitian ini juga mengusulkan suatu kerangka konseptual yang menghubungkan dimensi kognitif, motivasional, dan emosional dari proses pembelajaran, serta memosisikan AI sebagai kolaborator aktif dalam menumbuhkan rasa ingin tahu dan motivasi yang berkelanjutan.]

Keywords

Artificial Intelligence Education Motivation Self-Determination Self-Efficacy

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Enhancing Student Learning Motivation: An Artificial Intelligence Framework Grounded in Motivational Theory: Meningkatkan Motivasi Belajar Siswa: Kerangka Kecerdasan Buatan yang Berbasis Teori Motivasi. (2025). SiRad: Pelita Wawasan, 319-336. https://doi.org/10.64728/sirad.v1i3.art11
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How to Cite

Enhancing Student Learning Motivation: An Artificial Intelligence Framework Grounded in Motivational Theory: Meningkatkan Motivasi Belajar Siswa: Kerangka Kecerdasan Buatan yang Berbasis Teori Motivasi. (2025). SiRad: Pelita Wawasan, 319-336. https://doi.org/10.64728/sirad.v1i3.art11

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