Constructing a Socio-Legal Framework Proposal for Governing Large Language Model Usage and Application in Education
Abstract
:1. Introduction
2. Framing Current Achievements in the Field: State-of-the-Art
2.1. Current Soft Law Regulative Enacted Overview
- (a)
- Implementation of international or regional data protection regulations (GDPR) is necessary; otherwise, data protection could be questioned. Also, first of all, it is necessary to provide the legal framework for collecting and processing personal data.
- (b)
- Establish/revise the entire management system to implement the artificial intelligence strategy.
- (c)
- Implement special ethical regulations on artificial intelligence.
- (d)
- Adapt the laws regulating copyright. Only China, EU countries, and the United States have adapted their copyright laws.
- (e)
- Adapt national and local regulations to the emergence of artificial intelligence. This is a crucial issue in its use and application in different aspects of digitalized society.
- (f)
- Introduce the infrastructure for the proper use of GenAI in Education and research. All educational institutions should introduce an infrastructure that enables quality use of the system and its tools and avoids negative aspects.
- (g)
- Think about the long-term consequences of GenAI in Education and research.
- In order to achieve a thriving ecosystem of digital education, it is necessary to introduce quality infrastructure and establish mutual connectivity. Then, effective planning and development of digital capacities should be implemented to educate teaching staff who could impart quality knowledge and provide quality platforms with user-friendly tools.
- The achievement of the second strategic goal refers to the improvement of digital skills and competencies, and the following actions are foreseen: take care of digital literacy, with a particular emphasis on recognizing misinformation; education in the field of computing; good knowledge and understanding of artificial intelligence; increase the number of girls in digital studies and women in the field of digital careers (EU 2020).
2.2. Overview of LLM Usage Detection Technology
3. Methodology
Bibliometric Description of the Articles
4. Fundamental Governing Principles: Recommendations for Implementation of AI in Education
5. Discussion and Conclusions
- Ethical guidelines and standards. Values compose and construct all communities, so they are foreseen as the founding principle. Existing codes of ethics in education should be supplemented by crucial directions guiding new frontiers of fairness in AI implementation in education (Abdaljaleel et al. 2024; Bearman and Ajjawi 2023; Chan and Hu 2023; Chang et al. 2023; Chauncey and McKenna 2023; Gallent-Torres et al. 2023; J. Luo 2024; Michel-Villarreal et al. 2023; Rahman et al. 2023; Yan et al. 2024).
- Compliance upgrade. Overall, the vast existing system of regulation and standards across different aspects and functional parts of education need to be harmonized and adjusted to the AI framework. Most importantly, licensing, certification, quality, and safety standards must be reviewed and debated within a vast interdisciplinary expert network to ensure a comprehensive approach (Abdaljaleel et al. 2024; Bauer et al. 2023; Bearman and Ajjawi 2023; Dempere et al. 2023; Khan 2023; J. Luo 2024; Pham et al. 2023; Yan et al. 2024).
- Data governance. In our right to protection, security, and privacy, transparent, robust, and unbreachable systems to collect, store, and govern sensitive data are necessary for an AI-driven education system. Data anonymization, protection, and consent are critical factors in rethinking these aspects in AI-supplemented education (Bauer et al. 2023; Chan and Hu 2023; Chang et al. 2023; J. Luo 2024; Michel-Villarreal et al. 2023; Rejeb et al. 2024).
- AI systems’ transparency, understandability, and explainability are crucial in AI-involved decision-making processes that reflect different aspects of the educational system (e.g., student and teacher evaluation, hiring, and admission procedures) (Bearman and Ajjawi 2023; Chan and Hu 2023; Gallent-Torres, J. Luo 2024; Ng et al. 2024; Perkins and Roe 2023; Gallent-Torres et al. 2023; Tarisayi 2024; Lee et al. 2024; Prasad and Sane 2024; Xu et al. 2024).
- Algorithm accountability and transparency. Mutual (social) trust between developers, producers, maintenance services, and users is crucial in building confidence and openness towards system bias and error elimination (Michel-Villarreal et al. 2023; Chan and Hu 2023; Tarisayi 2024; Matthews and Volpe 2023).
- Quality control and auditing mechanisms are already well-established in continuous circles of accreditations, certifications, evaluations, revisions, and reviews embedded in educational settings. An amendment in existing procedures is needed to ensure AI-generated educational content fits the required users’ standards, needs, and expectations (Bearman and Ajjawi 2023; Tang et al. 2024; Bauer et al. 2023; Bearman and Ajjawi 2023; Hung and Chen 2023; Khan 2023; Thanh et al. 2023).
- Accessibility standards. Embedding AI educational content novelties cannot leave anyone behind. Inclusion and diversity standards must be considered, as must alternative formats and compatibility with assistive technologies intended for special-needs social groups (Abdaljaleel et al. 2024; Cowling et al. 2023; Dai et al. 2023; Matthews and Volpe 2023).
- Teacher training. The generational gap between digital nomads and “old school” teachers must be closed through mandated extensive teacher training and young researchers’ involvement reinforcement (Abdaljaleel et al. 2024; Chan and Hu 2023; Chiu 2023, 2024; Lo 2023; Ng et al. 2024).
- Research and innovation. Significant research is essential to determine the impact of generative AI on teaching, training, and learning outcomes. Longitudinal and comparative analysis, case studies, and best and past practice examples must precede the starting point or testing phase when introducing AI in education (Abdaljaleel et al. 2024; Chang et al. 2023; Cowling et al. 2023; Dai et al. 2023; Yan et al. 2024).
- Regulatory bodies and enforcement mechanisms need to be equipped and enforced to oversee the implementation of AI in education and regularly ensure ethical, regulatory, and legislative compliance (Chang et al. 2023; Gallent-Torres et al. 2023; Hung and Chen 2023; Khan 2023; H.-F. Li 2023; J. Luo 2024; Perkins and Roe 2023).
- Stakeholder initiative. As a unique asset that multiplies when shared, knowledge requires different perspectives to obtain the best performance. In aspiring to build a network of stakeholders with a unified goal of achieving excellence in AI deployment in education, stakeholder initiatives and perspectives can overcome new challenges (Abdaljaleel et al. 2024; Chang et al. 2023).
- Internationalization, interdisciplinarity, and harmonization of best practices and collaboration can strengthen resilience, bring new solutions, help overcome emerging issues and challenges, accomplish consistency, and improve alignment across diverse jurisdictions (Hung and Chen 2023; Khan 2023; Abdaljaleel et al. 2024; Chang et al. 2023; J. Luo 2024).
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Research Protocol | Detailed Description |
---|---|
Research various databases | Scopus Database and Web of Science |
Publication type | Peer-review journals and conference papers |
Language | All |
Date range | 2000–2024 |
Search fields | Title, abstract, and keywords |
Search terms (Scopus) | (TITLE-ABS-KEY (“ChatGPT”) AND TITLE-ABS-KEY (“education”) OR TITLE-ABS-KEY (“Policy”) OR TITLE-ABS-KEY (“regulation”) OR TITLE-ABS-KEY (“legislation”) |
Search terms (Web of Science) | ChatGPT (All Fields) AND Education (All Fields) AND Policy OR Regulation OR Legislation (All Fields) |
Original Database |
A total of 104 documents were found (104 documents and 0 duplicates) Articles = 76 Conference papers = 6 Editorials = 3 Errata = 1 Notes = 2 Reviews = 16 |
Added Database |
A total of 101 documents were found (101 documents and 0 duplicates) Article = 65 Articles in press = 14 Editorial materials = 6 Editorial materials; early access = 1 Proceedings papers = 5 Reviews = 9 Reviews; early access = 1 |
Merging Information |
A total of 142 documents were found (38 new documents from the added database) Articles = 105 Articles in press = 1 Conference papers = 6 Editorials = 3 Editorial materials = 3 Editorial materials; early access = 1 Errata = 1 Notes = 2 Proceedings papers = 2 Reviews = 18 |
Timespan | 2023–2024 |
---|---|
Total number of countries | 34 |
Total number of institutions | 140 |
Total number of sources | 110 |
Total number of references | 0 |
Total number of languages | 3 |
--English (# of docs) | 37 |
--Norwegian (# of docs) | 1 |
--Unknown (# of docs) | 104 |
Total number of documents | 142 |
--Articles | 105 |
--Articles in press | 1 |
--Conference papers | 6 |
--Editorials | 3 |
--Editorial materials | 3 |
--Editorial materials; early access | 1 |
--Errata | 1 |
--Notes | 2 |
--Proceedings papers | 2 |
--Reviews | 18 |
Average documents per author | 1.03 |
Average documents per institution | 5.69 |
Average documents per source | 1.27 |
Average documents per year | 71.0 |
Total number of authors | 710 |
Total number of authors’ keywords | 148 |
Total number of authors keywords plus | 109 |
Total single-authored documents | 19 |
Total multi-authored documents | 123 |
Average collaboration index | 4.42 |
Max H-index | 2 |
Total number of citations | 1922 |
Average citations per author | 2.71 |
Average citations per institution | 13.73 |
Average citations per document | 13.54 |
Average citations per source | 17.45 |
Author | Main Governing Recommendations |
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Abdaljaleel (Abdaljaleel et al. 2024) | Details on policy and strategies for AI integration in education whose key elements focus on:
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Bauer (Bauer et al. 2023) | Focuses on enhancing peer-feedback scenarios in higher education, encompassing key points through:
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Bearman (Bearman and Ajjawi 2023) | Reveals AI learning strategies through:
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Bukar (Bukar et al. 2024) | Constructs policy-making framework for generative AI through risk, reward and resilience categories:
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Chan (Chan and Hu 2023) | Outlines AI training, ethical use, and risk management as crucial components in AI implementation in higher education through the following requirements:
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Chang (Chang et al. 2023) | Suggests pedagogical principles for AI chatbot integration in education. The main observations deal with:
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Chauncey (Chauncey and McKenna 2023) | Emphasizes the importance of ethical AI chatbots in education. The main contributions include the following:
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Chiu (Chiu 2024) | Focuses on implications for policy assessment and development in educational institutions, emphasizing:
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Chiu (Chiu 2023) | Deals with AI educational aspects in school implementation, highlighting the importance of:
|
Cowling (Cowling et al. 2023) | Prospects on ChatGPT’s potential in higher-degree research amending
|
Dai (Dai et al. 2023) | Proposes a model for AI-enhanced postgraduate research, highlighting:
|
Galent (Gallent-Torres et al. 2023) | Explains pro and cons of AI use in education through three main categories:
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Hung (Hung and Chen 2023) | Aims to regulate ChatGPT’s use and application in academic settings through:
|
Kayali (Kayalı et al. 2023) | Highlights AI in education through two dimensions, risks and precautions, stressing the importance of:
|
Khanal (Khan 2023) | Reveals and problematizes big tech’s impact on public policy theory through:
|
Li (H.-F. Li 2023) | Outlines implications for practice and policy, leaning towards:
|
Lo (Lo 2023) | Brings anti-plagiarism guidelines urging for:
|
Luo (J. Luo 2024) | Recommends adapting higher education for generative AI through:
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Mathews (Matthews and Volpe 2023) | Is open to implementing generative AI in education, overcoming challenges, and finding aolutions in:
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Michel (Michel-Villarreal et al. 2023) | Debates on AI use in higher education, urging for:
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Ng (Ng et al. 2024) | Study on AI technologies for student-Centered learning and self-regulated learning while
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Perkins (Perkins and Roe 2023) | Focuses on academic integrity policies in higher-education institutions to:
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Pham (Pham et al. 2023) | Examines AI-assisted learning in engineering technology courses based on the following:
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Polyportis (Polyportis and Pahos 2024) | Extracts institutional policy and ChatGPT adoption to:
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Rahman (Rahman et al. 2023) | Promotes ethical ChatGPT usage in education, empowering
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Rejeb (Rejeb et al. 2024) | Develops educational I-institutions’ AI usage guidelines urging for:
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Tarisayi (Tarisayi 2024) | Aligns innovation with integrity in education through:
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Thanh (Thanh et al. 2023) | The proposed framework for generative AI assessments pointing toward key topics needs to:
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Yan (Yan et al. 2024) | Updates innovations for educational technology, undelaying
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Share and Cite
Mezak Matijevic, M.; Pisker, B.; Dokic, K. Constructing a Socio-Legal Framework Proposal for Governing Large Language Model Usage and Application in Education. Soc. Sci. 2024, 13, 479. https://doi.org/10.3390/socsci13090479
Mezak Matijevic M, Pisker B, Dokic K. Constructing a Socio-Legal Framework Proposal for Governing Large Language Model Usage and Application in Education. Social Sciences. 2024; 13(9):479. https://doi.org/10.3390/socsci13090479
Chicago/Turabian StyleMezak Matijevic, Mirela, Barbara Pisker, and Kristian Dokic. 2024. "Constructing a Socio-Legal Framework Proposal for Governing Large Language Model Usage and Application in Education" Social Sciences 13, no. 9: 479. https://doi.org/10.3390/socsci13090479