Aug 2026· Journal of Computers for Science and Mathematics Learning· Vol 3, pp. 118-131· 0 citations
TL;DR
The research comes to the conclusion that, when paired with curriculum goals and efficient instructional design, AI-supported digital tools should be seen as cognitive and pedagogical support systems rather than just technical advancements that might enhance science and math teaching.
Abstract
Particularly in science and math classes where students struggle with abstract thinking, conceptual comprehension, problem-solving, and sustained academic engagement, Artificial Intelligence (AI) has grown in importance as a component of instructional technology. As a result, AI-enabled digital solutions provide chances for data-driven teaching support, adaptive feedback, and personalized learning pathways. With a focus on conceptual comprehension, problem-solving skills, academic engagement, and self-directed learning, this research sought to determine the degree to which AI-powered digital tools improve students’ learning outcomes in science and mathematics. The study used a mixed-methods approach, combining qualitative information from student comments and classroom observations with quantitative assessments of academic progress. The research was conducted in upper secondary and postsecondary educational environments. Science and math classrooms are using AI-supported technology like data-driven instructional dashboards, intelligent feedback mechanisms, and adaptive learning systems. While qualitative data offered insight into student participation, classroom interaction, and perceived learning assistance, quantitative data were used to evaluate students’ learning performance before and after the intervention. The results show that using AI-supported digital tools enhanced learning outcomes, especially in courses that call for gradual conceptual expansion, abstract cognition, and iterative reasoning. Additionally, qualitative data indicated that students benefited from increased autonomy during learning activities, timely feedback, ongoing observation, and personalized learning paths. The research comes to the conclusion that, when paired with curriculum goals and efficient instructional design, AI-supported digital tools should be seen as cognitive and pedagogical support systems rather than just technical advancements that might enhance science and math teaching.
The study contributes user-derived design requirements that can guide the development of trustworthy and context-appropriate AI-supported learning platforms for undergraduate ICT students in programming-related courses at the two participating universities; broader generalization to other higher education fields requires further research.
Кazimova Dinara, Turmuratova Dinara, Zatyneyko Anatoly et al.· International Journal of Inf...· 0 citations
The rapid integration of artificial intelligence (AI) into education has introduced new possibilities for personalized, real-time instructional support, particularly through AI-driven scaffolded feedback. This study examines the impact of such feedback on students’ cognitive load and problem-solving efficiency, addressing a notable gap in existing literature, which has largely focused on academic achievement and technology acceptance in higher education settings rather than on cognitive mechanisms among younger learners. Grounded in Cognitive Load Theory, Vygotsky’s Sociocultural Theory of scaffolding, and Self-Regulated Learning Theory, the study explores how AI-generated feedback that is timely, individualized, and responsive to student performance can reduce extraneous cognitive load while promoting deeper engagement, self-regulation, and more efficient problem-solving. Employing a data-mining approach, the research draws on quantitative data collected through surveys, assessments, classroom observations, and records generated by AI-assisted learning platforms to identify patterns linking scaffolded feedback, cognitive performance, and problem-solving outcomes. Findings indicate that well-structured AI feedback systems help organize instructional content, minimize unnecessary cognitive strain, and support faster, more effective problem-solving, though challenges remain regarding technology access, teacher readiness, data privacy, and the need for continued oversight of AI-based instruction. The study concludes that while AI-driven scaffolded feedback holds significant promise for enhancing learning in K-8 classrooms, further research is needed to explore its long-term effects and to develop evidence-based strategies for effective implementation, particularly at the elementary school level.
Connie Ngujo, Precious Albao, Regina P. Galigao· International journal of hum...· 0 citations
This study developed and evaluated a technology-integrated discovery learning model to strengthen elementary students' mathematical conceptual understanding and learning engagement. Although digital technologies are increasingly available in primary classrooms, mathematics instruction frequently remains dominated by teacher-centered practices that prioritize procedural fluency over conceptual reasoning. To address this problem, the study employed a Research and Development design guided by the ADDIE framework, which comprises analysis, design, development, implementation, and evaluation. The participants included fifth-grade elementary students involved in small-group and large-group trials, a classroom teacher, and three expert validators specializing in mathematics education, instructional media, and language. Data were collected using expert validation sheets, teacher and student practicality questionnaires, classroom observations, and pretest-posttest assessments. The data were analyzed through descriptive statistics, normalized gain analysis, and paired-sample t-tests. The findings indicate that the developed model achieved a high level of validity, with an average expert validation score of 90%. Teacher and student responses also showed that the model was highly practical, with an average practicality score of 89%. Furthermore, implementation of the model substantially improved students' mathematics learning outcomes, as reflected in high N-gain scores of 0.81 in the small-group trial and 0.75 in the large-group trial. Paired-sample t-tests confirmed significant differences between pretest and posttest scores (p < .05). These findings suggest that integrating discovery learning with digital technology offers a pedagogically sound, student-centered, and conceptually meaningful approach to elementary mathematics instruction.
Artificial intelligence systems that adapt instruction to individual learners are increasingly deployed in K-12 classrooms, yet empirical evidence on their effects in authentic elementary settings remains limited, particularly for students with mathematics learning difficulties. This dissertation examines AI-powered personalized learning during primary school fraction instruction, a domain that is foundational to later mathematics and STEM achievement. The first manuscript presents a systematic review of research on artificial intelligence in mathematics education published between 2020 and 2024. The second manuscript reports a quasi-experimental study evaluating Mathbot, a chatbot-based personalized learning platform, against business-as-usual classroom instruction. Repeated measures ANOVA was used to assess change in fraction comprehension and situational interest across time points. Results indicated modest improvements in fraction comprehension for students using Mathbot relative to traditional instruction, while changes in situational interest were not statistically significant. Findings suggest that automated personalization did not displace the instructional role of the teacher and that teacher decision-making remained central to student outcomes. The work contributes classroom-based evidence to ongoing discussion about the capabilities and limits of adaptive AI systems in elementary mathematics, and about accessibility and equity considerations when such systems are used with students with disabilities.
This study investigates the effectiveness of integrating Artificial Intelligence (AI) within a Project-Based Inquiry (PBI) framework to enhance elementary students’ mathematical connections in geometry learning. AI functions as a cognitive scaffolding tool by providing adaptive prompts that support students’ reasoning, representation, and reflection, while PBI engages learners in authentic problem-solving tasks such as designing classroom layouts and park environments.The indicators of mathematical connections assessed in this study include: (1) connections between mathematical concepts (e.g., area and multiplication), (2) connections across multiple representations (visual, symbolic, and verbal), and (3) connections between mathematics and real-world contexts. This study employed a Design-Based Research (DBR) approach involving iterative cycles of design, implementation, evaluation, and refinement. Data were collected at the elementary school level through classroom observations, students’ project artifacts, short interviews, and AI–student interaction logs. The findings reveal substantial improvements in students’ mathematical communication and reasoning skills. Quantitative results indicate increases across all assessed dimensions, including precision of vocabulary usage (from 38% to 76%), logical structuring of arguments (from 42% to 71%), interactive discourse (from 36% to 68%), and accuracy in problem-solving (from 44% to 79).Qualitative findings further show that students transitioned from procedural to conceptual reasoning, demonstrating the ability to connect geometric concepts with real-world design contexts. AI-supported scaffolding facilitated this shift by guiding students from informal reasoning toward formal mathematical modeling. Interview data confirmed increased student engagement, metacognitive awareness, and confidence in problem-solving.These results suggest that AI-supported PBI effectively promotes interconnected mathematical understanding and meaningful learning experiences in elementary education.
F. Firdaus, Khairunnisa Zulfa Alifah· Frontiers in Education· 0 citations
Artificial Intelligence (AI) is transforming teaching–learning practices by enabling personalized, data-driven, and scalable educational environments aligned with Industry 4.0. AI-based tools such as adaptive learning systems, intelligent tutoring, automated assessment, simulations, and predictive analytics address key limitations of traditional education, including one-to-many instruction, delayed feedback, and limited learner engagement. Across disciplines engineering, healthcare, humanities, social sciences, and management—AI supports contextual and experiential learning through virtual labs, NLP-based feedback, decision-support systems, and immersive technologies. Beyond instruction, AI enhances institutional functions such as learner analytics, dropout prediction, curriculum optimization, and inclusive education. This paper reviews recent research and proposes an AI-Integrated Pedagogical Enhancement Model (AI-IPEM). Empirical findings indicate improvements in learning efficiency (22–45%), feedback turnaround time (70–90%), student retention (10–18%), and learning compliance (30–50%). The study concludes that AI serves as an enabler of augmented pedagogy, complementing teachers and fostering higher-order thinking, creativity, and lifelong learning.
Charles Arockiasamy, P. A· International Journal of Eme...· 0 citations
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