GENERATIVE ARTIFICIAL INTELLIGENCE AND ITS IMPACT ON LEARNING INITIATIVE, CRITICAL THINKING, AND SELF-COGNITION OF CRIMINOLOGY STUDENTS IN GINGOOG CITY
Aug 2026· Zenodo (CERN European Organization for Nuclear Research)
Artificial Intelligence in Healthcare and Education
Abstract
The increasing use of Generative Artificial Intelligence (GenAI) in higher education has changed how students learn, complete tasks, and access information. In criminology education, tools such as ChatGPT are used for concept clarification, writing support, and task completion. This study aimed to explore the lived experiences of criminology students in Gingoog City regarding their use of GenAI for learning and personal development. Specifically, it examined their experiences with AI, how AI shapes their learning initiatives, critical thinking, and self-awareness, and their aspirations for its future use. The study employed a qualitative phenomenological design. Data were gathered through in-depth interviews and analyzed using Braun and Clarke’s thematic analysis. The findings revealed that students viewed AI as a valuable academic support tool that helped them understand difficult concepts, improve writing, organize ideas, and reduce academic stress. However, they also experienced ethical tension, including guilt, reduced pride, and diminished ownership of AI-assisted outputs. While AI supports comprehension, idea generation, and reflection, excessive dependence on it may weaken learning initiative, critical thinking, and independent effort. Students also recognized AI’s limitations and practiced verifying its outputs. The study implies the need for a clear policy to promote the fair, responsible, and ethical use of AI in the Criminal Justice Program. This research was completed in July 2026.
The method, ECCOLA, is presented, which aims at making the high-level AI ethics principles more practical, making it possible for developers to more easily implement them in practice.
Ville Vakkuri, Kai-Kristian Kemell, P. Abrahamsson· EUROMICRO Conference on Soft...· 64 citations· ⚡6
The goal is to not only refine the accuracy of the LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education.
Z. Rasheed, Malik Abdul Sami, Muhammad Waseem et al.· arXiv.org· 62 citations· ⚡3
A comprehensive overview of how enhanced sampling methods are reshaping the field, with a particular focus on the data-driven construction of collective variables, is provided.
Kai Zhu, Enrico Trizio, Jintu Zhang et al.· Chemical Reviews· 58 citations
The use of large language models to automatically improve the user story quality in Austrian Post Group IT agile teams is explored, with a reference model for an Autonomous LLM-based Agent System developed and implemented at the company.
Zheying Zhang, M. Rayhan, Tomas Herda et al.· International Conference on...· 48 citations· ⚡4
This paper introduces a novel multi-AI-agent system designed to fully automate SLRs, and demonstrates how it substantially reduces the time and effort traditionally required for SLRs while maintaining comprehensiveness and precision.
Abdul Malik Sami, Z. Rasheed, Kai-Kristian Kemell et al.· arXiv.org· 44 citations· ⚡2
The proposed LLM-based multi-agent system automates qualitative data analysis process, creating opportunities for researchers and practitioners, and future improvements focus on enhancing multilingual performance and integrating continuous expert feedback.
Z. Rasheed, Muhammad Waseem, Aakash Ahmad et al.· arXiv.org· 41 citations
AI is making software generation faster, but speed does not remove the need for expertise. As more work is delegated to AI, tacit knowledge may become one of the most important human advantages in software engineering. The post Beyond Prompt Engineering: The Role of Tacit Knowledge in Software Engineering appeared first on GPT-Lab.
MIT News · Artificial Intelligence· news.mit.eduSep 16, 2026