Generative AI · Education leadership

Actionable companion · Staff capability and AI literacy

From evidence to district action: Human–AI Co-Agency for Competency-Based Talent Development: A Framework Integrating Creativity, Cognitive Skills, and Ethical Collaboration

AI-assisted editorial implementation guide. The district actions are proposed applications, not findings or recommendations attributed to the original authors. No additional peer review is asserted.

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Evidence and context

Confirm the study population, setting and task before applying its findings to your district.

Original authors’ abstract, reproduced under the linked source license:

Abstract The rapid integration of generative artificial intelligence (GenAI) into education offers significant opportunities for talent development; however, current applications remain predominantly tool-centric and lack grounding in cognitive and intelligence theory. To address this limitation, the present study introduces the Human-AI Co-Competency Framework for Education (HACC-E), a human-centered, mastery-driven model intended to promote the synergistic development of creativity and cognitive intelligence. Drawing on contemporary intelligence research, including executive function theory, creative cognition models, and socio-emotional competence frameworks, HACC-E positions AI as a cognitive amplifier that supports higher-order reasoning, reflective thinking, and adaptive problem-solving, rather than as a replacement for human judgment. Employing a design science research methodology, the framework integrates competency-based progression, AI-augmented formative feedback, and simulation-based experiential learning within a governance structure that ensures human oversight and ethical accountability. The model aligns fluid reasoning, systems thinking, creative ideation, emotional regulation, and collaborative intelligence with structured AI-mediated feedback loops and authentic performance evidence. Analytical validation confirms pedagogical coherence, measurable competency mapping, and mitigation of critical AI-related risks, including automation bias and overreliance. By operationalizing distributed human–AI agency within a mastery-based progression architecture, HACC-E advances a theoretically grounded approach to AI-enabled talent development. This framework contributes to the ongoing discourse on human-centered artificial intelligence by providing a scalable and ethically robust model for cultivating cognitive, creative, and socio-emotional intelligence in educational systems designed for future readiness. Keywords: artificial intelligence, education, simulation, human–AI co-agency, competency-based learning system

A 90-day district pilot

  1. Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
  2. Days 16–30 · Design. Run a staff workshop using three authentic lesson-planning tasks. Ask participants to identify hallucinations, revise outputs and document when they reject the tool. Name an accountable owner and set the baseline, success threshold and budget before implementation.
  3. Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
  4. Days 61–90 · Decide. Measure error-detection accuracy before and after training, preparation time and the quality of the final lesson plans. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.

The board decision brief

Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.

Attribution and reuse

Maha Alfaleh. Human–AI Co-Agency for Competency-Based Talent Development: A Framework Integrating Creativity, Cognitive Skills, and Ethical Collaboration. Journal of Intelligence (2026-07-09). https://doi.org/10.3390/jintelligence14070144

Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.