Artificial intelligence (AI), conversational agents, and generative AI: implications for adult education practice and research
Open accessAbstract
The AI era is upon us, and it is still within our power to ensure it brings prosperity for all. (Kristalina Georgieva, International Monetary Fund) Coined by McCarthy et al. (Citation1955), the term ‘artificial intelligence’ (AI) refers to the ability of a digital machine to emulate human cognition and decision-making. Ever since AI has transformed production systems and other jobs and non-job-related activities. Meanwhile, a new research area has emerged: AI in Education (AIED), which studies how teaching and learning practices and program development may ‘benefit’ from applying AI technologies like intelligent tutoring systems, chatbots, and automated assessment.
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Recent advances in Generative Artificial Intelligence, particularly through Large Language Models, offer promising new opportunities for mitigating these limitations by providing dynamic, context-sensitive, and human-like feedback at scale.
“Recent advances in Generative Artificial Intelligence (GenAI), particularly through Large Language Models (LLMs), offer promising new opportunities for mitigating these limitations by providing dynamic, context-sensitive, and human-like feedback at scale (Bozkurt, 2023; Milana et al., 2024).”
From Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback (2026) · cites this paper as Milana et al., 2024
The 'Conversational agents’ use in education' section says chatbots can provide personalised feedback and help teachers tailor support to students’ needs. The 'AI technologies and conversational agents' section says LLM-based chatbots use human-like language. The copy does not establish that this feedback is provided at scale or that it mitigates the specific limitations referenced in the citing sentence; no page numbers were supplied. (LLM, conf=0.84)
Experimental — accuracy not yet measuredRecent developments in LLMs offer promising opportunities to address limitations in traditional feedback practices.
“Recent developments in LLMs offer promising opportunities to address these limitations (Bozkurt, 2023; Milana et al., 2024).”
From Supporting self-regulated learning through generative AI feedback in online higher education: the importance of student perceptions of the source of feedback (2026) · cites this paper as Milana et al., 2024
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- Authors
- Milana, M., Brandi, U., Hodge, S., & Hoggan-Kloubert, T.
- Affiliations
- University of Verona (IT)
- Aarhus University (DK)
- Griffith University (AU)
- University of Augsburg
- Corresponding author
- Milana, Marcella
- Venue
- International Journal of Lifelong Education
- Publisher
- Informa UK Limited
- Published
- 2024-01-02
- Volume
- 43
- Issue
- 1
- Pages
- 1-7
- ISSN
- 0260-1370 (ISSN-L)
- 1464-519X
- OpenAlex
- W4391522328
- Cited by
- 46
- References
- 22
- Citations by year
- 2026: 24 · 2025: 16 · 2024: 6
- FWCI
- 17.30
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Lifelong Education (publishedVersion)
- Scopus (Elsevier) (submittedVersion, other-oa)
- Griffith Research Online (submittedVersion, cc-by-nc)
- OPUS (Augsburg University) (submittedVersion, cc-by-nc)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- artificial intelligence in education, chatbots, generative AI, adult education, intelligent tutoring systems, automated assessment, lifelong learning, AI adoption in education
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