Artificial Intelligence Technologies in Education: Benefits, Challenges and Strategies of Implementation

• URL: http://arxiv.org/abs/2102.09365v1
• Date: Thu, 11 Feb 2021 11:09:41 GMT
• Title: Artificial Intelligence Technologies in Education: Benefits, Challenges and Strategies of Implementation
• Authors: Mieczys{\l}aw L. Owoc, Agnieszka Sawicka, Pawe{\l} Weichbroth
• Abstract summary: We have identified the benefits and challenges of implementing artificial intelligence in the education sector. We have also reviewed modern AI technologies for learners and educators, currently available on the software market. We have developed a strategy implementation model, described by a five-stage, generic process, along with the corresponding configuration guide.
• Score: 8.54335661175611
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Since the education sector is associated with highly dynamic business environments which are controlled and maintained by information systems, recent technological advancements and the increasing pace of adopting artificial intelligence (AI) technologies constitute a need to identify and analyze the issues regarding their implementation in education sector. However, a study of the contemporary literature reveled that relatively little research has been undertaken in this area. To fill this void, we have identified the benefits and challenges of implementing artificial intelligence in the education sector, preceded by a short discussion on the concepts of AI and its evolution over time. Moreover, we have also reviewed modern AI technologies for learners and educators, currently available on the software market, evaluating their usefulness. Last but not least, we have developed a strategy implementation model, described by a five-stage, generic process, along with the corresponding configuration guide. To verify and validate their design, we separately developed three implementation strategies for three different higher education organizations. We believe that the obtained results will contribute to better understanding the specificities of AI systems, services and tools, and afterwards pave a smooth way in their implementation.

Related papers

• 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)
• Bringing Generative AI to Adaptive Learning in Education [58.690250000579496]
  We shed light on the intersectional studies of generative AI and adaptive learning. We argue that this union will contribute significantly to the development of the next-stage learning format in education.
  arXiv  Detail & Related papers  (2024-02-02T23:54:51Z)
• Multimodality of AI for Education: Towards Artificial General Intelligence [14.121655991753483]
  multimodal artificial intelligence (AI) approaches are paving the way towards the realization of Artificial General Intelligence (AGI) in educational contexts. This research delves deeply into the key facets of AGI, including cognitive frameworks, advanced knowledge representation, adaptive learning mechanisms, and the integration of diverse multimodal data sources. The paper also discusses the implications of multimodal AI's role in education, offering insights into future directions and challenges in AGI development.
  arXiv  Detail & Related papers  (2023-12-10T23:32:55Z)
• Machine Unlearning: A Survey [56.79152190680552]
  A special need has arisen where, due to privacy, usability, and/or the right to be forgotten, information about some specific samples needs to be removed from a model, called machine unlearning. This emerging technology has drawn significant interest from both academics and industry due to its innovation and practicality. No study has analyzed this complex topic or compared the feasibility of existing unlearning solutions in different kinds of scenarios. The survey concludes by highlighting some of the outstanding issues with unlearning techniques, along with some feasible directions for new research opportunities.
  arXiv  Detail & Related papers  (2023-06-06T10:18:36Z)
• New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution [2.94944680995069]
  ChatGPT's high performance on standardized academic tests has thrust the topic of artificial intelligence (AI) into the mainstream conversation about the future of education. This research aims to investigate the potential impact of AI on education through review and analysis of the existing literature across three major axes: applications, advantages, and challenges.
  arXiv  Detail & Related papers  (2023-05-12T08:22:54Z)
• Proactive and Reactive Engagement of Artificial Intelligence Methods for Education: A Review [2.2843885788439793]
  We investigate how artificial intelligence, machine learning and deep learning methods are being utilized to support students, educators and administrative staff. We consider the involvement of AI-driven methods in the education process in its entirety.
  arXiv  Detail & Related papers  (2023-01-23T02:47:36Z)
• Selected Trends in Artificial Intelligence for Space Applications [69.3474006357492]
  This chapter focuses on differentiable intelligence and on-board machine learning. We discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT)
  arXiv  Detail & Related papers  (2022-12-10T07:49:50Z)
• Competency Model Approach to AI Literacy: Research-based Path from Initial Framework to Model [0.0]
  Research on AI Literacy could lead to an effective and practical platform for developing these skills. We propose and advocate for a pathway for developing AI Literacy as a pragmatic and useful tool for AI education.
  arXiv  Detail & Related papers  (2021-08-12T15:42:32Z)
• Confronting Structural Inequities in AI for Education [5.371816551086117]
  We argue that the dominant paradigm of evaluating fairness on the basis of performance disparities in AI models is inadequate for confronting the structural inequities that educational AI systems (re)produce. We demonstrate how educational AI technologies are bound up in and reproduce historical legacies of structural injustice and inequity, regardless of the parity of their models' performance.
  arXiv  Detail & Related papers  (2021-05-18T22:13:35Z)
• 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.