CoChat
Sign Up
Cited by 422Artificial Intelligence in EducationArtificial Intelligence ApplicationsArtificial Intelligence in Healthcare and Education

Empowering Educators to Be AI-Ready

Open access
Luckin, Rosemary, Cukurova, Mutlu, Kent, Carmel, du Boulay, Benedict·2022·Computers and Education Artificial Intelligence
DOI 10.1016/j.caeai.2022.100076
Open this paper in CoChat — free accountOpen PDF

Abstract

In this paper, we present the concept of AI Readiness, along with a framework for developing AI Readiness training. ‘AI Readiness’ can be framed as a contextualised way of helping people to understand AI, in particular, data-driven AI. The nature of AI Readiness training is not the same as merely learning about AI. Rather, AI Readiness recognises the diversity of the professions, workplaces and sectors for whom AI has a potential impact. For example, AI Readiness for lawyers may be based on the same principles as AI Readiness for Educators. However, the details will be contextualised differently. AI Readiness recognises that such contextualisation is not an option: it is essential due to the multiple intricacies, sensitivities and variations between different sectors and their settings, which all impact the application of AI. To embrace such contextualisation, AI Readiness needs to be an active, participatory training process and aims to empower people to be more able to leverage AI to meet their needs. The text that follows focuses on AI Readiness within the Education and Training sector and starts with a discussion of the current state of AI within education and training, and the need for AI Readiness. We then problematize the concept of AI Readiness, why AI Readiness is needed, and what it means. We expand upon the nature of AI Readiness through a discussion of the difference between human and Artificial Intelligence, before presenting a 7-step framework for helping people to become AI Ready. Finally, we use an example of AI Readiness in action within Higher Education to exemplify AI Readiness.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.caeai.2022.100076

Cites

65 of 65 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

  • Developing tailored interventions empowers educators to create learning environments that embrace AI's potential while safeguarding academic integrity and preparing students for future careers.

    “Moreover, developing tailored interventions empowers educators to create learning environments that embrace AI's potential while safeguarding academic integrity and preparing students for the evolving demands of their future careers (Luckin et al., 2022).”

    From Six Institutional Intervention Areas to Support Ethical and Effective Student Use of Generative AI in Higher Education: A Narrative Review (2026) · cites this paper as Luckin et al., 2022

    Section 2 (page unavailable) says education must respond to AI-driven changes in future careers and help people benefit safely from AI. Section 1.2 (page unavailable) describes educator-facing tools that suggest interventions and monitor academic integrity. The paper does not establish that developing tailored interventions itself safeguards academic integrity or prepares students for future careers. (LLM, conf=0.85)

    Experimental — accuracy not yet measured

  • The GenAI Readiness Framework proposed by Luckin, Cukurova [61] is a structured, step-by-step approach offering a practical pathway for staff to achieve GenAI literacy.

    “The GenAI Readiness Framework proposed by Luckin, Cukurova [61], is a structured, step-by-step approach, offering a practical pathway for staff to achieve this literacy.”

    From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as Luckin, Cukurova [61]

    Section 4 presents a seven-step AI Readiness Framework designed to help organisations engage their staff and develop AI readiness. The concluding discussion says it provides a structure for AI training. The source does not identify it as a GenAI-specific framework or say that completing it achieves GenAI literacy. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

  • The interconnectedness of educational systems means that changes in one area can have far-reaching impacts.

    “The authors acknowledge that the interconnectedness of educational systems means that changes in one area can have far-reaching impacts, which must be considered when integrating GenAI into existing teaching methodologies [61].”

    From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as [61]

  • Luckin et al. (2022) presented a framework for developing AI readiness training for educators, including seven steps (excite, tailor and hone, identify, collect, apply, learn, and iterate).

    “Luckin et al. (2022) presented a framework for developing AI readiness training for educators, including seven steps (excite, tailor and hone, identify, collect, apply, learn, and iterate).”

    From Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs (2024) · cites this paper as Luckin et al., 2022

  • The perceived usefulness of technology in the classroom is a strong predictor of intentions to participate in technology-related professional development.

    “Studies have suggested that the perceived usefulness of technology in the classroom is a strong predictor of intentions to participate in technology-related professional development (Fütterer et al., 2023a; Luckin et al., 2022).”

    From Artificial intelligence in higher education: exploring faculty use, self-efficacy, distinct profiles, and professional development needs (2024) · cites this paper as Luckin et al., 2022

    The Introduction discusses teachers’ preferences in AI tools, and §3.1 mentions professional development, but neither links perceived classroom usefulness to intentions to participate in training. Searches for “perceived usefulness,” “professional development,” and teacher participation intentions found no passage reporting the claimed predictor relationship. The indexed passages provide section labels but no page numbers. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 11

  1. Alvero, A. J., Arthurs, N., antonio, A. L., Domingue, B. W., Gebre-Medhin, B., Giebel, S., et al. (2020). AI and holistic review: Informing human reading in college admissions. In Proceedings of the AAAI/ACM conference on AI (pp. 200-206). Ethics, and Society.
  2. Azevedo, R., & Aleven, V. (2013). International handbook of metacognition and learning technologies (Vol. 26). Springer International Handbooks of Education. Springer.
  3. Educate Ventures research and Cambridge University Press. (2021). Shock to the system: Lessons from covid-19. Available at https://www.cambridge.org/partnership/insigh ts/shock-system-lessons-learned-covid-19/.
  4. Hutchins, E. (2000). Distributed cognition. International encyclopedia of the social and behavioral sciences. Elsevier Science.
  5. Kent, C., Chaudhry, M. A., Cukurova, M., Bashir, I., Pickard, H., Jenkins, C., et al. (2021). Machine learning models and their development process as learning affordances for humans. In International conference on artificial intelligence in education (pp. 228-240). Springer, Cham.
  6. Kent, C., & du Boulay, B. (2022). AI for learning. Abingdon, UK: CRC Press.
  7. Liao, Q. V., Gruen, D., & Miller, S. (2020). Questioning the AI: Informing design practices for values for machine learning-based systems (pp. 1-14). ACM CHI.
  8. Li, H., Gobert, J., & Dickler, R. (2019). Evaluating the transfer of scaffolded inquiry: What sticks and does it last?. In International conference on artificial intelligence in education (pp. 163-168). Cham: Springer.
  9. Smith, C. E., et al. (2020). Keeping community in the loop: Understanding wikipedia stakeholder explainable AI user experiences. ACM CHI, 1-15.
  10. Vygotsky, L. S. (1986). Thought and language. Cambridge, Mass: The MIT Press.
  11. Wirth, R., & Hipp, J. (2000). CRISP-DM: Towards a standard process model for data mining. In , Vol. 1. Proceedings of the 4th international conference on the practical applications of knowledge discovery and data mining. London, UK: Springer-Verlag.

Cited by

Sorted by citations

Related

Authors
Luckin, R., Cukurova, M., Kent, C., & du Boulay, B.
Affiliations
University College London (GB)
Corresponding author
Luckin, Rosemary
ORCID
Venue
Computers and Education Artificial Intelligence
Publisher
Elsevier BV
Published
2022
Volume
3
Pages
100076
ISSN
2666-920X (ISSN-L)
DOI
10.1016/j.caeai.2022.100076
OpenAlex
W4281692409
Cited by
422
References
65
Citations by year
2026: 130 · 2025: 154 · 2024: 104 · 2023: 29 · 2022: 5
FWCI
72.25
Type
journal article
Language
en
Open access
Licence
Locations
  • Computers and Education Artificial Intelligence (publishedVersion, cc-by-nc-nd)
  • Open Research Online - ORO (The Open University) (acceptedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • UCL Discovery (University College London) (submittedVersion)
Funders
Arizona State University
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
AI readiness, educators, AI literacy, data-driven AI, human-AI distinction, participatory training, higher education, artificial intelligence in education

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