Generative Artificial Intelligence and Agents in Research and Teaching

• URL: http://arxiv.org/abs/2508.16701v2
• Date: Tue, 26 Aug 2025 10:23:02 GMT
• Title: Generative Artificial Intelligence and Agents in Research and Teaching
• Authors: Jussi S. Jauhiainen, Aurora Toppari,
• Abstract summary: This study provides a comprehensive analysis of the development, functioning, and application of generative artificial intelligence (GenAI) and large language models (LLMs)<n>It traces the conceptual evolution from artificial intelligence through machine learning (ML) and deep learning (DL) to transformer architectures.<n>Central to the analysis are the ethical, social, and environmental challenges posed by GenAI.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This study provides a comprehensive analysis of the development, functioning, and application of generative artificial intelligence (GenAI) and large language models (LLMs), with an emphasis on their implications for research and education. It traces the conceptual evolution from artificial intelligence (AI) through machine learning (ML) and deep learning (DL) to transformer architectures, which constitute the foundation of contemporary generative systems. Technical aspects, including prompting strategies, word embeddings, and probabilistic sampling methods (temperature, top-k, and top-p), are examined alongside the emergence of autonomous agents. These elements are considered in relation to both the opportunities they create and the limitations and risks they entail. The work critically evaluates the integration of GenAI across the research process, from ideation and literature review to research design, data collection, analysis, interpretation, and dissemination. While particular attention is given to geographical research, the discussion extends to wider academic contexts. A parallel strand addresses the pedagogical applications of GenAI, encompassing course and lesson design, teaching delivery, assessment, and feedback, with geography education serving as a case example. Central to the analysis are the ethical, social, and environmental challenges posed by GenAI. Issues of bias, intellectual property, governance, and accountability are assessed, alongside the ecological footprint of LLMs and emerging technological strategies for mitigation. The concluding section considers near- and long-term futures of GenAI, including scenarios of sustained adoption, regulation, and potential decline. By situating GenAI within both scholarly practice and educational contexts, the study contributes to critical debates on its transformative potential and societal responsibilities.

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