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Ardian Asyhari
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AI and Developmental Insights in Education (AIDIE)
Published by Foundation of Advanced Education
ISSN : -     EISSN : 31235220     DOI : https://doi.org/10.58524/aidie
Core Subject :
Focus and Scope of AI and Developmental Insights in Education AIDIE AI and Developmental Insights in Education AIDIE is an international peer reviewed journal that focuses on the integration of Artificial Intelligence AI in educational settings with a particular emphasis on its implications for developmental psychology and learning sciences The journal aims to bridge the gap between cutting edge AI technologies and their practical applications in enhancing cognitive social and emotional development within educational environments AIDIE provides a platform for researchers educators policymakers and technology developers to share theoretical and empirical research that contributes to the evolving landscape of AI enhanced education The journal welcomes interdisciplinary research that intersects AI psychology and education emphasizing innovation and evidence based practices Focus Areas AI Driven Personalized and Adaptive Learning Development of AI algorithms for personalized learning experiences Adaptive learning systems that cater to individual learning styles and paces Machine learning models for predicting student performance and providing tailored interventions Intelligent tutoring systems and adaptive feedback mechanisms Cognitive and Emotional Development Through AI The role of AI in supporting cognitive skill acquisition in various educational contexts AI assisted emotional intelligence development and socio emotional learning SEL AI based interventions for students with special educational needs AI driven analytics to assess cognitive load and mental well being AI Based Assessment and Feedback Systems Automated grading systems and their effectiveness in formative and summative assessments Natural Language Processing NLP for assessing written responses and personalized feedback The role of AI in formative assessment and continuous feedback mechanisms Ethical and bias considerations in AI based assessments Developmental Psychology Insights in AI Education The integration of developmental theories into AI driven educational tools The influence of AI on cognitive social and emotional growth in learners Developmental perspectives on student engagement and motivation in AI driven classrooms Longitudinal studies on the impact of AI on learning development Gamification and AI in Education The use of AI in developing educational games that enhance motivation and engagement AI driven analytics in gamified learning environments The role of reinforcement learning in educational gamification Impact of AI enhanced gamification on student achievement and retention Ethical and Psychological Implications of AI in Education Privacy and data security concerns in AI driven educational tools Ethical considerations related to AI bias fairness and transparency Psychological effects of AI reliance in the learning process Policy implications of AI adoption in educational institutions Collaborative AI Frameworks for Teaching and Learning Integration of AI with traditional pedagogical methods AI driven collaboration tools for peer learning and group projects Social robotics in educational environments to facilitate teamwork and social learning Enhancing teacher effectiveness through AI supported instructional strategies AI and Teacher Professional Development AI driven teacher training programs and competency building Utilizing AI to analyze teaching effectiveness and classroom dynamics Personalized recommendations for professional growth using AI analytics Ethical considerations in AI supported teacher evaluations AI in Online and Distance Learning Environments Intelligent virtual learning environments VLEs and their impact on learning outcomes AI driven chatbots and virtual assistants for online student support Remote assessment techniques using AI tools Strategies for increasing engagement in online learning with AI Big Data and Learning Analytics in Education Leveraging AI for educational data mining and pattern recognition Predictive analytics for student success and dropout prevention AI enhanced dashboards for educators to track student progress The role of AI in data driven decision making for institutional improvements Scope of the Journal AIDIE welcomes submissions of original research articles theoretical papers systematic reviews case studies and short communications that address but are not limited to the following topics Artificial Intelligence in Education Applications and innovations in AI technology to enhance learning processes Developmental and Educational Psychology The impact of AI on cognitive emotional and social development in learners of all ages Technology Enhanced Learning AI assisted tools and platforms that support teaching and learning Data Driven Education Utilizing AI to analyze and optimize learning outcomes through big data analytics Human AI Interaction in Education Understanding how students and educators interact with AI tools and their effectiveness Ethical Considerations in AI Integration Exploring the challenges and frameworks for ethical AI implementation in educational settings Pedagogical Strategies for AI Adoption Developing frameworks to integrate AI into teaching methodologies Cross Cultural Studies in AI and Education Investigating the role of AI in diverse educational contexts and cultural settings Types of Manuscripts Accepted Original Research Articles Empirical studies that present new findings related to AI applications in educational psychology and learning sciences Review Articles Comprehensive reviews that summarize and critically analyze existing research in the field Case Studies Practical implementations of AI driven educational solutions with in depth analysis and reflections Short Communications Brief reports on emerging trends innovative tools and ongoing research projects in AI and education Theoretical and Conceptual Papers Papers that propose new models frameworks or theoretical perspectives on AI and developmental education Target Audience AIDIE is intended for a diverse readership that includes but is not limited to Academics and researchers in AI psychology and education Educators and instructional designers interested in AI driven teaching methodologies Policymakers and administrators exploring the role of AI in shaping education systems EdTech developers focused on designing AI based educational tools and solutions Publication Frequency and Open Access Policy AIDIE is published two times a year in May and November and follows an open access policy ensuring that all published articles are freely available to the global community without subscription fees thereby promoting widespread dissemination of knowledge
Arjuna Subject : -
Articles 15 Documents
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Teachers’ Perceptions of AI Tools for Enhancing Student Motivation and Learning Engagement in Higher Education Hermanto Hermanto; Mukarramah Mustari; Athiyyah Sepriani
AI and Developmental Insights in Education Vol. 1 No. 1 (2025): AI and Developmental Insights in Education
Publisher : CV. FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/aidie.v1i1.53

Abstract

This study examined higher education teachers’ perceptions of AI tools for enhancing student motivation and learning engagement in response to growing interest in AI-supported instruction. Using an explanatory sequential mixed methods design, quantitative data were collected from 98 instructors across three universities in Lampung Province, followed by qualitative interviews with 15 purposively selected participants. Survey measures assessed perceived usefulness, ease of use, motivational impact, engagement impact, and ethical concerns. Quantitative results showed strong perceived motivational benefits of AI and moderate engagement effects, with significant correlations between usefulness and motivation (p < .001) and disciplinary differences in engagement perceptions (p = .019). Qualitative thematic analysis revealed that teachers observed increased confidence and task persistence among students using AI tools but noted uneven engagement linked to digital readiness and expressed concerns about privacy, shallow reasoning, and academic integrity. Integrated findings indicated that while AI is viewed as a supportive motivational resource, its pedagogical value depends on ethical safeguards and student competencies. The study contributes insights into how teachers interpret AI’s educational role, highlighting implications for institutional policy, professional development, and future AI-enhanced learning designs.
Exploring Student Readiness for AI-Assisted Learning: A Preliminary Study in Indonesian Universities Happy Komikesari; Fijira Pasyah
AI and Developmental Insights in Education Vol. 1 No. 1 (2025): AI and Developmental Insights in Education
Publisher : CV. FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/aidie.v1i1.57

Abstract

This study examined the emerging educational challenge of students’ readiness for AI-assisted learning in Indonesian universities, focusing on how cognitive, technological, and affective factors shape their preparedness to engage with AI-supported instructional environments. Using a quantitative, cross-sectional survey design, data were collected from 189 undergraduate students across diverse academic programs using validated AI readiness scales administered through an online questionnaire. Descriptive and inferential analyses revealed moderate to high readiness levels overall, with prior exposure to AI tools showing significant associations with cognitive and technological readiness, while gender and study major did not produce meaningful differences. Effect sizes indicated that experiential familiarity contributed more strongly to readiness than demographic variables. These findings highlight the developmental need to strengthen AI literacy and equitable digital access in higher education. The study offers empirical insights to guide curriculum design, institutional policy, and future research on responsible and developmentally aligned AI integration.
The Potential of AI Chatbots as Learning Companions: Early Insights from Students’ Cognitive and Emotional Responses Adhie Thyo Priandika; Permata Permata
AI and Developmental Insights in Education Vol. 1 No. 1 (2025): AI and Developmental Insights in Education
Publisher : CV. FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/aidie.v1i1.58

Abstract

This study examined the growing educational challenge of understanding how students cognitively and emotionally experience AI chatbots when used as learning companions. Using a convergent mixed-methods design, data were collected from 189 university students through Likert-scale measures of cognitive support, emotional response, and perceived effectiveness, along with 186 written reflections analyzed using thematic analysis. Quantitative results showed strong perceptions of cognitive clarity, positive emotional experiences, and significant associations among the three constructs, indicating that students who felt cognitively supported also viewed the chatbot as more effective. Qualitative themes reinforced these findings by revealing that students valued the chatbot’s step-by-step explanations and experienced a sense of emotional safety when asking questions. Integrated analysis demonstrated convergence across strands, highlighting the intertwined cognitive and emotional dimensions of chatbot-assisted learning. The study contributes early evidence that AI chatbots can function as supportive learning companions with meaningful implications for AI-enhanced education.
Teachers’ Perceptions and Experiences of Integrating AI-Based Tools in Classroom Practices in Indonesia Hendrik Pratama; Laila Fitriana
AI and Developmental Insights in Education Vol. 1 No. 1 (2025): AI and Developmental Insights in Education
Publisher : CV. FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/aidie.v1i1.59

Abstract

The increasing presence of artificial intelligence (AI) in education raises important questions about preservice teachers’ readiness to integrate AI into future instructional practice. This study examined the extent to which attitudes toward AI predict readiness for AI integration. A quantitative, cross-sectional survey design was used with 212 preservice teachers from an Indonesian university. Data were collected using a validated 22-item Attitudes Toward AI in Education Scale measuring perceived usefulness, ethical and privacy concerns, pedagogical confidence, and professional identity. Descriptive statistics, confirmatory factor analysis, and multiple regression were conducted. Results showed that all four attitudinal dimensions significantly predicted AI readiness, with perceived usefulness emerging as the strongest positive predictor and ethical concerns demonstrating a negative association. These findings highlight the multidimensional nature of AI readiness and underscore the importance of addressing both competence and ethical awareness in teacher preparation. The study contributes empirical evidence to support AI literacy development in teacher education.
Understanding Preservice Teachers’ Attitudes Toward AI in Education: Implications for Future Professional Development Tiyas Abror Huda; Sodikin Sodikin
AI and Developmental Insights in Education Vol. 1 No. 1 (2025): AI and Developmental Insights in Education
Publisher : CV. FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/aidie.v1i1.60

Abstract

This study examined the educational problem of how preservice teachers perceive Artificial Intelligence (AI) in education and the extent to which their attitudes predict readiness to use AI in future teaching practice. Using a quantitative, cross-sectional survey design, data were collected from 212 preservice teachers enrolled in teacher-education programs. Participants completed the 22-item Attitudes Toward AI in Education Scale, which measured perceived usefulness, ethical and privacy concerns, pedagogical confidence, and professional identity readiness. Descriptive statistics indicated generally positive attitudes, while regression analyses showed that perceived usefulness and pedagogical confidence significantly predicted AI readiness, ethical concerns had a small negative effect, and professional identity readiness was not significant. These results highlight the central role of confidence and perceived value in shaping readiness, while also underscoring the need to address ethical apprehensions in teacher preparation. The study contributes a validated measurement framework and offers evidence-based guidance for designing AI-focused professional development in teacher education.
Simple AI Tools for Personalized Learning: A Qualitative Exploration of Classroom Experiences Ardian Asyhari; Ghifar Javad H Aziz; Siti Amelia Agustin
AI and Developmental Insights in Education Vol. 1 No. 2 (2025): AI and Developmental Insights in Education
Publisher : CV. FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/aidie.v1i2.76

Abstract

This study investigates how teachers experience the use of simple artificial intelligence (AI) tools in supporting personalized learning, addressing the broader educational challenge of integrating emerging technologies into everyday classroom practice. Using a qualitative phenomenological design, the study draws on semi-structured interviews, classroom observations, and instructional documents from twelve primary and secondary school teachers who regularly employed AI-assisted text simplifiers, writing tools, and automated feedback generators. Data were analyzed using thematic analysis to capture patterns in teachers’ perceptions, instructional decisions, and strategies for validating AI outputs. Three core themes emerged: AI as a catalyst for differentiated content creation, shifts in the feedback process that enable deeper instructional dialogue, and the continued importance of teacher judgment in curating and contextualizing AI-generated material. These findings highlight how AI can enhance personalization and instructional efficiency while reinforcing the pedagogical and ethical role of the teacher in maintaining accuracy and cultural relevance. The study contributes to ongoing debates about AI-enhanced teaching by clarifying how every day, low-complexity tools reshape teacher agency, professional identity, and classroom practice.
The Role of AI in Supporting Student Self-Regulated Learning: Evidence from Early Classroom Implementations Ricco Herdiyan Saputra; Fredi Ganda Putra
AI and Developmental Insights in Education Vol. 1 No. 2 (2025): AI and Developmental Insights in Education
Publisher : CV. FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/aidie.v1i2.77

Abstract

This study examined how artificial intelligence supports students’ self-regulated learning during early classroom implementation, addressing the need to understand how emerging educational technologies influence learners’ planning, monitoring, and reflection processes. Using a convergent mixed methods design, quantitative survey data from 98 students were combined with qualitative reflections from 112 participants. The survey measured planning, monitoring, and reflection, while the qualitative strand captured students’ descriptions of how they engaged with AI-generated guidance. Results showed strong effects of AI on planning and reflection, with moderate and more variable patterns in monitoring. Integrated findings revealed convergence across strands for planning and reflection but divergence in monitoring, where students described difficulties interpreting feedback. These results suggest that AI can serve as a meaningful metacognitive scaffold when supported by developmentally appropriate guidance. The study contributes evidence on how AI influences learner regulation in authentic settings and highlights implications for instructional design and future research.
AI-Assisted Feedback in Online Learning: Students’ Experiences, Preferences, and Perceived Benefits Adyt Anugrah; Yani Suryani
AI and Developmental Insights in Education Vol. 1 No. 2 (2025): AI and Developmental Insights in Education
Publisher : CV. FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/aidie.v1i2.85

Abstract

This study examined how students experience and interpret AI-assisted feedback in online learning, addressing the growing need to understand its cognitive, emotional, and developmental implications. Using a convergent mixed-methods design, data were collected from 212 undergraduate students through a structured questionnaire including Likert-scale items and open-ended responses. Quantitative analyses provided descriptive and inferential results on students’ experiences, preferences, and perceived benefits, while qualitative thematic analysis identified patterns related to clarity, explanatory value, confidence building, and concerns about accuracy. Integrated findings showed strong convergence across strands, indicating that students generally valued AI feedback for its immediacy and usefulness, yet remained cautious about its limitations. The study concludes that AI-assisted feedback can support learning processes when designed to provide explanatory depth and align with instructional expectations. These insights contribute to research on AI-enhanced education by clarifying how learners engage with automated feedback and by highlighting design considerations for future implementation.
Investigating the Readiness of Schools to Adopt AI Technologies: A Case Study Approach Poniman Poniman; Windo Dicky Irawan
AI and Developmental Insights in Education Vol. 1 No. 2 (2025): AI and Developmental Insights in Education
Publisher : CV. FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/aidie.v1i2.86

Abstract

Schools are increasingly expected to adopt artificial intelligence (AI) technologies, yet little is known about how prepared they are to integrate these tools into teaching and learning. This study examined the institutional, pedagogical, and infrastructural factors that shape school readiness for AI adoption. Using a qualitative multiple-case study design, data were collected from 21 participants across three schools through semi-structured interviews, classroom observations, and document analysis. Reflexive thematic analysis guided the analytic process. Three core themes emerged: leadership vision and structural readiness, teacher pedagogical readiness, and infrastructural and ethical preparedness. Although school leaders expressed strong enthusiasm for AI, formal policies and implementation mechanisms were limited. Teachers demonstrated varying levels of confidence and conceptual clarity regarding AI, and infrastructural constraints, alongside the absence of ethical governance structures, further hindered readiness. These findings show that AI adoption is influenced by the dynamic interaction of organizational culture, professional competence, and resource conditions. The study contributes a nuanced, contextually grounded understanding of AI readiness and offers guidance for developing strategic, ethical, and pedagogically meaningful approaches to AI integration in schools.
Ethical Awareness of Educators Toward AI Usage: A Study on Bias, Privacy, and Responsible Implementation Wardani Wardani; Mutia Velli Agusta
AI and Developmental Insights in Education Vol. 1 No. 2 (2025): AI and Developmental Insights in Education
Publisher : CV. FoundAE

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58524/aidie.v1i2.87

Abstract

The increasing use of artificial intelligence in education has intensified concerns regarding algorithmic bias, data privacy, and the ethical responsibilities of educators, yet little is known about how educators understand and respond to these ethical challenges. This quantitative study examined the extent to which educators’ awareness of bias and privacy predicts their commitment to responsible AI implementation. A total of 214 educators participated in a cross-sectional survey that included validated measures of bias awareness, privacy awareness, and responsible implementation. Data were collected online and analyzed using descriptive statistics, correlations, and multiple regression. Results showed that both bias awareness and privacy awareness were significant predictors of responsible AI use, with the model explaining 46% of the variance. Educators reported moderate to high awareness across ethical domains, and effect sizes indicated meaningful relationships among variables. These findings highlight the central role of ethical literacy in shaping how educators adopt and regulate AI tools in classroom contexts. The study contributes a data-driven understanding of ethical awareness in AI-mediated education and underscores the need for professional development and policy frameworks that equip educators to navigate emerging ethical challenges.

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