Rethinking the entwinement between artificial intelligence and human learning: What capabilities do learners need for a world with AI?
Open accessAbstract
The proliferation of AI in many aspects of human life—from personal leisure, to collaborative professional work, to global policy decisions—poses a sharp question about how to prepare people for an interconnected, fast-changing world which is increasingly becoming saturated with technological devices and agentic machines. What kinds of capabilities do people need in a world infused with AI? How can we conceptualise these capabilities? How can we help learners develop them? How can we empirically study and assess their development? With this paper, we open the discussion by adopting a dialogical knowledge-making approach. Our team of 11 co-authors participated in an orchestrated written discussion. Engaging in a semi-independent and semi-joint written polylogue, we assembled a pool of ideas of what these capabilities are and how learners could be helped to develop them. Simultaneously, we discussed conceptual and methodological ideas that would enable us to test and refine our hypothetical views. In synthesising these ideas, we propose that there is a need to move beyond AI-centred views of capabilities and consider the ecology of technology, cognition, social interaction, and values.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The foundational competencies of understanding basic concepts, recognizing tools, and naming broad risks such as bias, hallucination, and privacy do not adequately prepare graduates, educators, or institutions for the strategic, critical, and ethical demands of an AI-saturated world.
“the foundational competencies of understanding basic concepts, recognizing tools, and naming broad risks such as bias, hallucination, and privacy do not adequately prepare graduates, educators, or institutions for the strategic, critical, and ethical demands of an AI-saturated world (Markauskaite et al., 2022).”
From From AI Literacy to AI Fluency: A Developmental Framework for Higher Education (2026) · cites this paper as Markauskaite et al., 2022
The citation lists haven’t been fetched yet
References 118 · Cited by 353
- Authors
- Markauskaite, L., Marrone, R., Poquet, O., Knight, S., Martinez-Maldonado, R., Howard, S., Tondeur, J., De Laat, M., Buckingham Shum, S., Gašević, D., & Siemens, G.
- Affiliations
- University of South Australia (AU)
- Inserm (FR)
- University of Technology Sydney (AU)
- University of Wollongong (AU)
- Vrije Universiteit Brussel (BE)
- Monash University (AU)
- ORCID
- Venue
- Computers and Education: Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2022
- Volume
- 3
- Pages
- 100056
- ISSN
- 2666-920X (ISSN-L)
- OpenAlex
- W4213430857
- Cited by
- 353
- References
- 118
- Citations by year
- 2026: 68 · 2025: 139 · 2024: 110 · 2023: 31 · 2022: 5
- FWCI
- 70.52
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
- Adelaide Research & Scholarship (AR&S) (University of Adelaide) (submittedVersion, cc-by-nc-nd)
- Monash University Research Portal (Monash University) (submittedVersion, other-oa)
- Research Online (University of Wollongong) (submittedVersion)
- UniSA Research Outputs Repository (University of South Australia) (submittedVersion, other-oa)
- HAL (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- VUBIR (Vrije Universiteit Brussel) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Digital Education and Society
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Psychology › Developmental and Educational Psychology › Innovative Teaching and Learning Methods
- Keywords
- artificial intelligence, human learning, AI literacy, polylogue, capability development, cognition, social interaction, values
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review