Why does AI unlock new possibilities in STEM education? A Bibliometric Analysis of Trends and Future Agenda
- URL: http://arxiv.org/abs/2607.05412v1
- Date: Wed, 10 Jun 2026 08:31:13 GMT
- Title: Why does AI unlock new possibilities in STEM education? A Bibliometric Analysis of Trends and Future Agenda
- Abstract summary: This study employs bibliometric methods to analyze 242 publications from 2015-2025.<n>The findings show that the field has transformed from intelligent tutoring systems to inquiry-based learning.<n>In this sense, AI is a core driving force promoting its shift from knowledge transmission to capability development.
- Score: 4.235919533481997
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: STEM education faces challenges in personalization and interdisciplinary integration. AI technology has brought new possibilities, but the mechanisms by which AI reshapes the STEM education ecosystem require systematic investigation. This study employs bibliometric methods to analyze 242 publications from 2015-2025, constructing knowledge maps to reveal the evolutionary trajectory. The findings show that the field has transformed from intelligent tutoring systems to inquiry-based learning and computational thinking cultivation driven by LLMs. AI's key contribution lies in providing intelligent scaffolding that lowers the threshold for understanding knowledge. In this sense, AI is a core driving force promoting its shift from knowledge transmission to capability development.
Related papers
- Why AI systems don't learn and what to do about it: Lessons on autonomous learning from cognitive science [79.50667364149979]
We critically examine the limitations of current AI models in achieving autonomous learning and propose a learning architecture inspired by human and animal cognition.<n>The proposed framework integrates learning from observation (System A) and learning from active behavior (System B) while switching flexibly between these learning modes as a function of internally generated meta-control signals (System M).
arXiv Detail & Related papers (2026-03-16T14:54:56Z) - Perspectives and potential issues in using artificial intelligence for computer science education [0.0]
ChatGPT has ignited widespread interest in Large Language Models (LLMs) and broader Artificial Intelligence (AI) solutions.<n>While AI technologies hold potential for enhancing learning experiences, there are also emerging concerns.<n>These include the risk of over-reliance on technology, the potential erosion of fundamental cognitive skills, and the challenge of maintaining equitable access to such innovations.
arXiv Detail & Related papers (2025-09-17T06:34:23Z) - AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research [58.944125758758936]
The Science of Science (SoS) explores the mechanisms underlying scientific discovery.<n>The advent of artificial intelligence (AI) presents a transformative opportunity for the next generation of SoS.<n>We outline the advantages of AI over traditional methods, discuss potential limitations, and propose pathways to overcome them.
arXiv Detail & Related papers (2025-05-17T15:01:33Z) - From MOOC to MAIC: Reshaping Online Teaching and Learning through LLM-driven Agents [78.15899922698631]
MAIC (Massive AI-empowered Course) is a new form of online education that leverages LLM-driven multi-agent systems to construct an AI-augmented classroom.
We conduct preliminary experiments at Tsinghua University, one of China's leading universities.
arXiv Detail & Related papers (2024-09-05T13:22:51Z) - Elucidating STEM Concepts through Generative AI: A Multi-modal
Exploration of Analogical Reasoning [5.759606494344033]
This study explores the integration of generative artificial intelligence (AI) with multi-modal analogical reasoning.
We have developed a novel system that utilizes the capacities of generative AI to transform intricate principles in mathematics, physics, and programming into comprehensible metaphors.
arXiv Detail & Related papers (2023-08-21T04:00:56Z) - The Future of Fundamental Science Led by Generative Closed-Loop
Artificial Intelligence [67.70415658080121]
Recent advances in machine learning and AI are disrupting technological innovation, product development, and society as a whole.
AI has contributed less to fundamental science in part because large data sets of high-quality data for scientific practice and model discovery are more difficult to access.
Here we explore and investigate aspects of an AI-driven, automated, closed-loop approach to scientific discovery.
arXiv Detail & Related papers (2023-07-09T21:16:56Z) - An interdisciplinary conceptual study of Artificial Intelligence (AI)
for helping benefit-risk assessment practices: Towards a comprehensive
qualification matrix of AI programs and devices (pre-print 2020) [55.41644538483948]
This paper proposes a comprehensive analysis of existing concepts coming from different disciplines tackling the notion of intelligence.
The aim is to identify shared notions or discrepancies to consider for qualifying AI systems.
arXiv Detail & Related papers (2021-05-07T12:01:31Z) - Distributed and Democratized Learning: Philosophy and Research
Challenges [80.39805582015133]
We propose a novel design philosophy called democratized learning (Dem-AI)
Inspired by the societal groups of humans, the specialized groups of learning agents in the proposed Dem-AI system are self-organized in a hierarchical structure to collectively perform learning tasks more efficiently.
We present a reference design as a guideline to realize future Dem-AI systems, inspired by various interdisciplinary fields.
arXiv Detail & Related papers (2020-03-18T08:45:10Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.