arXiv subjects

Ercan Erkalkan

Publications and source records attributed to Ercan Erkalkan.

3 recordsLinked to original sources

arXiv2026

Psychological Determinants of Academic Integrity in the Use of Generative AI in Higher Education

This paper examines the psychological determinants that shape academically honest and dishonest uses of generative artificial intelligence (GenAI) in higher education. Rather than treating academic misconduct as a purely technological problem, the study conceptualizes academic integrity as a psychologically mediated decision process influenced by moral reasoning, perceived social norms, policy clarity, academic self-efficacy, AI literacy, performance pressure, and beliefs about authorship. Methodologically, the paper adopts a focused narrative review and conceptual synthesis design. A purposive corpus of 16 core publications, including peer-reviewed studies and policy-oriented texts published between 2022 and March 2026, was assembled through targeted searches using combinations of the keywords generative AI, academic integrity, academic misconduct, moral disengagement, AI literacy, and higher education. The reviewed literature suggests that students do not interpret all forms of AI assistance as cheating. Integrity risk increases when institutional guidance is vague, peer use appears normalized, academic pressure is high, and AI tools are perceived as legitimate substitutes for difficult cognitive labor. By contrast, assignment-level guidance, explicit disclosure norms, ethics-oriented instruction, and authentic assessment design appear to reduce integrity risk more effectively than detection-centered responses alone. Based on these findings, the paper proposes an integrative conceptual model in which institutional context shapes psychological appraisal, and psychological appraisal in turn influences disclosed, borderline, or dishonest GenAI use. The paper concludes that effective responses to GenAI-related integrity problems should combine policy clarity, pedagogy, AI literacy, and student support rather than relying only on prohibition or software-based surveillance.

cs.CY↗

arXiv2025

Psychological Factors Influencing University Students Trust in AI-Based Learning Assistants

Artificial intelligence (AI) based learning assistants and chatbots are increasingly integrated into higher education. While these tools are often evaluated in terms of technical performance, their successful and ethical use also depends on psychological factors such as trust, perceived risk, technology anxiety, and students general attitudes toward AI. This paper adopts a psychology oriented perspective to examine how university students form trust in AI based learning assistants. Drawing on recent literature in mental health, human AI interaction, and trust in automation, we propose a conceptual framework that organizes psychological predictors of trust into four groups: cognitive appraisals, affective reactions, social relational factors, and contextual moderators. A narrative review approach synthesizes empirical findings and derives research questions and hypotheses for future studies. The paper highlights that trust in AI is a psychological process shaped by individual differences and learning environments, with practical implications for instructors, administrators, and designers of educational AI systems.

cs.HC↗

arXiv2025

Thermal RGB Fusion for Micro-UAV Wildfire Perimeter Tracking with Minimal Comms

This study introduces a lightweight perimeter tracking method designed for micro UAV teams operating over wildfire environments under limited bandwidth conditions. Thermal image frames generate coarse hot region masks through adaptive thresholding and morphological refinement, while RGB frames contribute edge cues and suppress texture related false detections using gradient based filtering. A rule level merging strategy selects boundary candidates and simplifies them via the Ramer Douglas Peucker algorithm. The system incorporates periodic beacons and an inertial feedback loop that maintains trajectory stability in the presence of GPS degradation. The guidance loop targets sub 50 ms latency on embedded System on Chip (SoC) platforms by constraining per frame pixel operations and precomputing gradient tables. Small scale simulations demonstrate reductions in average path length and boundary jitter compared to a pure edge tracking baseline, while maintaining environmental coverage measured through intersection merge analysis. Battery consumption and computational utilization confirm the feasibility of achieving 10, 15 m/s forward motion on standard micro platforms. This approach enables rapid deployment in the field, requiring robust sensing and minimal communications for emergency reconnaissance applications.

cs.CV↗