Objective: To develop a competence-based model (HCAI Block Model) that guides effective teaching, learning, and research of Human-Centered Artificial Intelligence in K-12 education.
Methods: The model was developed through four iterative phases using face validity, adapting the original programming Block Model through two lenses:
- A data science lens incorporating Computational Thinking 2.0
- A human-centered lens integrating ethical considerations
Key Findings:
- The HCAI Block Model consists of four layers: Data, Modeling, Trustworthiness, and Production/Inference
- Each layer incorporates knowledge, skills, and dispositions components
- The model maintains the original Block Model's key features of simplicity, constructive orientation, and communicative language
- Ethics and human-centered approaches are integrated throughout rather than being add-ons
Implications: The model provides a foundation for:
- Developing and evaluating AI teaching pedagogies
- Creating systematic approaches to teaching AI in K-12
- Integrating technical and ethical considerations in AI education
- Supporting teachers in developing both content knowledge and pedagogical content knowledge
Limitations:
- The model is newly developed and hasn't been extensively tested in practical settings
- Validation is primarily based on face validity rather than empirical testing
- Focus on K-12 context may limit broader applicability
Future Directions: Validation of the model through trials of various appropriate pedagogies with cohorts of students using the HCAI Block as the foundation.
Title and Authors: "HCAI Block Model: A competence model for Human Centred Artificial Intelligence at K-12" by Brian Conway, Keith Nolan, and Keith Quille
Published On: December 02-03, 2024
Published By: HCAIep '24 (Human Centred Artificial Intelligence - Education and Practice) Conference