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Sponsored Artificial Intelligence: A Modern Approach, Global Edition Artificial Intelligence: A Modern Approach, Global Edition

Author

V. Katsouros

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Review Open access Jul 2026

Artificial intelligence in everyday classrooms: Effects of a purpose-built AI study assistant on secondary students’ learning, motivation, and engagement

Background Recent advances in artificial intelligence (AI) have accelerated the development of educational technologies intended to support students’ learning processes. However, classroom-based evidence on the educational impact of purpose-built AI applications remains limited, particularly across national contexts. Methods This study examined the effects of an AI conversational study assistant (Study Buddy) on secondary students’ academic performance, motivation, and engagement in authentic school settings. A quasi-experimental design was employed across two case studies conducted in Cyprus (Grade 10 Physics, N = 47) and Greece (Grade 7 History, N = 70). In each context, intact classes were assigned to experimental and control conditions. Academic performance was assessed using curriculum-aligned teacher-developed tests, while motivation and engagement were measured using the Motivation and Engagement Survey. In addition, system-generated usage logs were analyzed descriptively to document student access, interaction intensity, and patterns of tool use during the intervention. Statistical analyses included descriptive statistics and parametric or non-parametric group comparisons depending on distributional assumptions. Results Students who used Study Buddy demonstrated significantly higher learning gains in Physics and higher post-test scores in History compared to peers in control groups. In contrast, no statistically significant differences were observed for motivation or engagement in either case study. Usage log analysis indicated that students actively engaged with the application and that interaction patterns reflected the instructional design of each case study. Conclusions The findings suggest that purpose-built AI study assistants can support academic learning when integrated into regular classroom instruction. However, short-term exposure and predominantly task-focused interactions may limit their influence on motivational and engagement-related outcomes. The study contributes classroom-based, cross-national evidence on educational AI tools and highlights the importance of instructional design and teacher mediation in shaping both usage patterns and learning outcomes.

Theodoros Karafyllidis, Anna Vacalopoulou, S. Stamouli et al. · 0 citations
#natural language process... Preprint Sep 2026

Automatic Lyric Transcription for Greek Songs: Scaling and Task Composition Effects in Whisper Adaptation

Automatic Lyric Transcription (ALT) remains substantially more challenging than speech recognition due to melodic variability, rhythmic irregularity, and accompaniment interference. This is heightened in low-resource languages like Greek, where no prior benchmark for ALT exists. We present the first controlled study of Whisper adaptation for Greek ALT, investigating model scaling effects, task composition via multitask training in transcribe-translate ratios, and two-stage speech-to-singing adaptation. We also curate a segment-level aligned singing dataset based on the Greek Audio Dataset (GAD) using source separation and CTC forced alignment. Results show that scaling consistently improves performance, while multitask learning acts as a beneficial regularizer primarily for smaller-capacity models. The 2-stage adaptation in Whisper Large-v3 achieves a Word Error Rate (WER) of 27.2%, a significant improvement over zero-shot baselines, establishing the first Greek ALT benchmark.

Maria Frangiadaki, Dimitrios Damianos, Kosmas Kritsis et al. · 0 citations
Open access Aug 2026

A Security-Oriented Lifecycle Model for Large Language Model Systems

A lifecycle model for LLM systems is proposed that supports security analysis by structuring it around security-relevant boundaries rather than workflow optimisation, and is supported by a 12-stage LLMOps pillar and a 9-category governance pillar.

Eleftherios Batzolis, George Drosatos, V. Katsouros et al. · 0 citations

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