Agentic education · Learning science

Actionable companion · Feedback & scaffolding

Learning with agency: Designing an AI-Responsive Early Childhood Curriculum: A Conceptual Framework for Supporting Developmental Learning Trajectories in Preschool Education

AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.

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Evidence and context

Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Early childhood · preschool, Children · stage unspecified, Adult & professional learning. Verify the participant population and task before transferring the findings.

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

Background The rapid integration of artificial intelligence (AI) and deep learning technologies into education creates new opportunities for personalized learning in early childhood education (ECE). Yet limited research has examined how educators interpret and enact AI-responsive curriculum decisions in preschool settings, particularly in culturally diverse contexts. Purpose This study explores educators’ experiences in designing and implementing AI-responsive curricula and develops a conceptual framework explaining how AI can inform curriculum decision-making for young children without displacing teacher judgment. Method An interpretive phenomenological study was conducted at TK Harapan Bangsa, Makassar, Indonesia, with nine adult participants: six preschool teachers, two curriculum specialists, and one AI tool developer-collaborator whose expert interview supplied technical context. Data comprised two in-depth interviews with each teacher, interviews with curriculum specialists and the developer-collaborator, 18 non-participant observations, and analysis of AI-supported planning documents. MAXQDA 2024 supported data management. Giorgi’s phenomenological method was operationalized through first-order meaning units, second-order interpretive themes, and third-order composite constructs. Triangulation, reflexive memoing, an audit trail, member checking, and peer debriefing supported trustworthiness. Findings Three interconnected constructs emerged. Predictive Developmental Tracking describes AI-supported identification of developmental readiness and possible concerns. AI-Responsive Curriculum Adaptation captures teachers’ active interrogation and contextual modification of algorithmic recommendations. Deep Learning as Pedagogical Scaffold positions AI as a support that amplifies, rather than replaces, professional expertise. Together, the constructs informed the AI-Responsive Curriculum Design (ARCD) Framework. Conclusion The ARCD Framework explains AI-responsive curriculum implementation as a relational process combining predictive insight, ecological sensitivity, and educator agency. It offers implications for curriculum policy, teacher professional development, and ethical AI integration in ECE.

What kind of evidence is this?

Supporting AI learning science · Qualitative / mixed methods

Stage: Early childhood · preschool; Children · stage unspecified; Adult & professional learning. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.

Study methods · source passage

Purposive maximum-variation sampling ( Patton, 2015) was used to include differences in professional experience, developmental-domain expertise, and prior AI engagement. The study included nine adult participants: 1. Six ECE classroom educators with two to fifteen years of preschool experience. All held at least a Diploma IV or bachelor’s degree in ECE. These educators were the primary phenomenological participants because they directly experienced AI-supported planning and classroom decision-making. 2. Two curriculum specialists affiliated with Dinas Pendidikan Kota Makassar. They contributed institutional and curriculum-development perspectives. 3. One AI tool developer-collaborator. This participant completed a single extended expert interview and served as an additional technical informant; the interview contextualized system design and did not replace educator accounts as the principal experiential evidence.

Limitations and uncertainty · source passage

Early childhood development is domain-interdependent, non-linear, and profoundly influenced by relational and environmental contexts ( Shonkoff & Phillips, 2000). Cognitive, language-literacy, social-emotional, physical-motor, and value-moral development do not occur in isolation. This systemic quality challenges tools built around narrow or reductive performance indicators.

Design the system before the pilot

Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.

  • Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.

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. Use a brief adult-mediated story, turn-taking or language activity. An educator controls every interaction; the agent never becomes an unsupervised companion. Preserve hands-on play and human conversation. 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 independent naming, explanation or turn-taking in a human-led activity after the tool is removed. Record frustration, overstimulation and the amount of adult help. 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

Herlina Herlina; Chandra Apriyansyah; Rusmayadi Rusmayadi; Herman Herman. Designing an AI-Responsive Early Childhood Curriculum: A Conceptual Framework for Supporting Developmental Learning Trajectories in Preschool Education. F1000Research (2026-10-06). https://doi.org/10.12688/f1000research.185645.2

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.