Date
Publisher
arXiv
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.
Study at a glance
- Question: How can generative artificial intelligence and specialized AI agents support the full academic research and teaching process?
- Setting and data: Demonstration using 2024 survey data from 105 Ukrainian refugees in Estonia and simulated educational course planning scenarios.
- Method: Conceptual analysis and practical demonstration using specialized generative AI agents across research and teaching workflows.
Why it matters
Generative artificial intelligence and autonomous AI agents are rapidly entering academic research and education, raising questions about how they work, how they can be used effectively, and what risks they pose.
Key findings
- A chain of specialized generative AI agents completed an entire empirical research workflow, from ideation to report writing, within a single working day.
- Instructional AI agents successfully supported the complete teaching cycle, generating syllabi, lecture slides, interactive exercises, personalized tutoring responses, and automated feedback.
- Generative AI models face notable limitations and risks, including factual errors, bias from training data, lack of explainability, and heavy environmental energy demands.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
What is the application?
Why use AI?
Study design
