Frontiers in Education: Digital Learning Innovations

This paper examines whether OpenClaw, an open-source AI agent framework, can serve as the foundation for adaptive educational AI systems. We analyse OpenClaw's architecture against established requirements for adaptive learning: student modelling, curriculum delivery, difficulty adjustment, and assessment feedback. A proposed framework maps OpenClaw's core features (persistent memory, modular skills, and retrieval-augmented generation) onto educational functions including adaptive tutoring, self-regulated learning support, and data sovereignty compliance. We extend the analysis with (a) a three-year total-cost-of-ownership comparison between local graphics processing unit (GPU) deployment and frontier cloud application programming interfaces (APIs), (b) a concrete integration path with open-source Item Response Theory libraries (py-irt, mirt-Python, EduCDM) to give difficulty adjustment a psychometric foundation, (c) a multimodal accessibility layer combining open-source automatic speech recognition (ASR; Whisper) and text-to-speech (TTS; Coqui) for learners with visual or motor impairments, and (d) a Consolidated Standards of Reporting Trials (CONSORT)-style design for a single-course feasibility pilot. Resultssuggest that OpenClaw offers significant advantages in cost, customisation, and data control, while challenges remain in technical deployment complexity, psychometric maturity, and the absence of empirical validation. The paper concludes with a concrete pilot protocol and specific research directions for classroom implementation.

Difficulties in connecting visual representations with formal deductive reasoning continue to hinder students’ success in geometry proof construction. This study examined the association between generative artificial intelligence (AI)-supported instruction and Senior High School students’ geometric reasoning and proof construction. A quasi-experimental pre-test-post-test non-equivalent control group design was employed involving 86 students from two intact classes in a public senior high school in Ghana. The experimental group received generative AI-supported instruction, while the control group received conventional instruction. Data were collected using a Geometry Reasoning and Proof Test and a perception and attitude questionnaire. Independent-samples t-tests, paired-samples t-tests, ANCOVA, and descriptive statistics were used for data analysis. The findings revealed that students exposed to generative AI-supported instruction achieved significantly higher geometric reasoning and proof construction scores than students receiving conventional instruction, even after controlling for pre-test performance. Students in the experimental group also reported favorable perceptions and attitudes toward the instructional approach. The study contributes to the growing literature on AI-supported mathematics learning by demonstrating the potential of generative AI to function as a cognitive scaffold that supports the transition from visual intuition to deductive reasoning in geometry. The findings suggest that generative AI-supported instruction may provide a useful instructional approach for enhancing students’ geometric reasoning and proof construction.

Generative Artificial Intelligence (AI) is increasingly embedded in student support systems across primary and secondary education (K-12) and higher education, offering new possibilities for personalized learning, adaptive feedback, and scalable academic assistance. At the same time, its integration raises complex ethical challenges that extend beyond technical issues to core educational values. This conceptual paper examines ethical dilemmas associated with AI-powered student support, focusing on how they appear differently across educational contexts. While concerns such as data privacy, equity and algorithmic bias, transparency, accountability, and the preservation of human relationships are shared across sectors, their implications are context sensitive. In K–12 education, ethical priorities center on developmental vulnerability, child protection, parental consent, and teacher mediation, while in higher education, attention shifts toward student autonomy, academic integrity, surveillance, and institutional accountability. Ethical frameworks often generalize across educational levels, overlooking main developmental and institutional differences. This study provides/suggests a structured, context-sensitive ethical framework to guide the responsible implementation of AI-powered student support across educational settings.

IntroductionOnline learning effectiveness depends on pedagogical design rather than platform capabilities. This study evaluates the BOOOM model (Boosting Cognition, Online Collaboration, Organizing Instruction, Overseeing Metacognition, Motivating) in teacher education.MethodsA quasi-experimental pretest-posttest control group design was employed with 90 students (Group B: experimental, n = 45; Group A: control, n = 45) over 12 weeks in Google Classroom. Data were collected through achievement tests, engagement scales, self-efficacy scales, satisfaction questionnaires, and interviews.ResultsThe BOOOM model exerted a statistically significant positive effect on academic achievement, cognitive and behavioral engagement, and pedagogical self-efficacy: F(1, 87) = 28.54, p < .001, partial η² = .247. No significant differences were found for emotional engagement or platform usability.DiscussionThe effectiveness of online learning depends more on the quality of pedagogical design than on the platform itself. The BOOOM model is proposed as a practical structured approach for active learning in teacher education.

The rapid integration of Artificial Intelligence (AI) into higher education underscores the urgent need to assess and foster robust AI literacy among future teachers. This cross-disciplinary competence is a critical, ethical, and equity-oriented foundation that empowers educators to personalize instruction and promote inclusive learning. However, current literature exhibits a methodological gap due to the scarcity of validated Spanish psychometric instruments. Furthermore, previous studies often evaluate pre-service teachers homogeneously, overlooking specialization-specific asymmetries and hindering the development of tailored, realistic, and effective curricula. To address this limitation, this study validates the Spanish adaptation of the Meta AI Literacy Scale (MAILS) to multidimensionally assess AI literacy. The 34-item instrument—measuring application, understanding, detection, ethics, creation, self-efficacy, and self-competence—was administered to a sample of 471 students in the Faculty of Education at the University of Salamanca across Sport and Exercise Sciences (48.6%), Primary Education (34.4%), Early Childhood Education (11.9%), and minor academic tracks (5.1%), providing high ecological validity. Psychometric validation demonstrated excellent internal consistency (overall Cronbach's alpha = .903). Overall, students exhibited strengths in practical application and self-efficacy, but only moderate levels of theoretical understanding and detection. ANOVA tests revealed significant competency disparities across seven of the nine assessed dimensions. Sport Sciences students excelled in creation (M = 7.62) and application (M = 17.22) due to their frequent use of performance-monitoring technologies. Conversely, Early Childhood Education students exhibited high digital vulnerability, scoring lowest in creation (M = 5.41), application (M = 14.38), and problem-solving self-efficacy (M = 6.34). Primary Education students demonstrated the highest resistance to algorithmic persuasion (M = 9.65). Transversely, the ethics dimension showed no significant differences (F = 0.053, p = .948), reflecting a shared, highly developed deontological awareness. In conclusion, these findings highlight the need to abandon one-size-fits-all digital training in higher education. Designing tailored curricular pathways that address the specific epistemological, technical, and affective demands of each degree is essential. Only through targeted preparation can AI literacy drive equity and social justice, empowering future educators to implement AI, open educational resources, and Universal Designfor Learning frameworks safely, ethically, and inclusively.