Using Explainable AI and Hierarchical Planning for Outreach with Robots

Source publication / research team or educational organization described in paper

Not specified in extracted text

Researchers, educators, instructors, or facilitators as described in the source publication

• In-school (K-12)
• Higher education
• Informal learning

Curriculum design or implementation

Not specified in extracted text

ing more prevalent in our daily lives. However, teaching non-experts, such as K-12 students, the complexities of robot planning can be challeng- ing. This work presents an open-source platform, JEDAI.Ed, that si

Robotics / physical AI, Explainable AI / robustness, Assessment / tutoring analytics

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

K-12, Higher education

Mixed or not explicitly specified; infer from target learner group and intervention design.

Varies by intervention; not specified unless the paper explicitly describes prerequisites.

• Document the AI education intervention, course, tool, or resource described in the source publication.
• Extract the learner context, AI role, pedagogy, outcomes, and constraints for AAB registry comparison.
• Understanding how robots plan and execute tasks is crucial in today’s world, where they are becoming more prevalent in our daily lives.

• Support AAB comparison across AI literacy, AI education, teacher training, higher education, and workforce contexts.
• Capture evidence maturity, transferability, and limitations rather than treating the publication as product endorsement.

• Not an AAB endorsement of the tool, curriculum, provider, or result.
• Not a direct replication record unless the source paper reports implementation details sufficient for replication.

Robotics / physical AI, Explainable AI / robustness, Assessment / tutoring analytics

Language context discussed in source publication

• Tutor
• Automation tool

• Primary interaction pattern inferred from publication: Curriculum / course design, Learning tool / resource design, Teacher professional development, Assessment support, Outreach / informal learning, Physical AI / robotics learning.
• AI capability focus: Robotics / physical AI, Explainable AI / robustness, Assessment / tutoring analytics.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.

• Review the publication’s reported context, learner group, AI tool or curriculum, implementation process, and outcome evidence.
• Map the case to AAB registry fields for comparison across educational levels and AI capability types.
• Use the source publication and PDF for any manual verification before public registry release.

• Human educators/researchers remain responsible for instructional design, supervision, interpretation, and ethical safeguards.
• AI systems or AI concepts provide the learning object, support tool, evaluator, simulator, or automation context depending on the paper.

• Instructional / curriculum-based learning
• Registry extraction emphasizes explicit learning goals, observed outcomes, constraints, and safety limitations.

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

• Case classified under: Published curriculum / implementation paper.
• Pedagogical pattern: Instructional / curriculum-based learning.
• Any additional adaptations should be verified against the full paper before public-facing publication.

• Engagement evidence should be interpreted according to the source paper’s reported method and sample.
• Finally, JEDAI.Ed, includes an adaptive curriculum generation method that provides stu- dents with customized learning ramps.

• Finally, JEDAI.Ed, includes an adaptive curriculum generation method that provides stu- dents with customized learning ramps.

Understanding how robots plan and execute tasks is crucial in today’s world, where they are becoming more prevalent in our daily lives. However, teaching non-experts, such as K-12 students, the complexities of robot planning can be challeng- ing.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.

• Design / conceptual evidence

• Can be used as an AAB evidence record for cross-case comparison, standards drafting, and evidence-maturity mapping.
• Supports identification of recurring patterns in AI literacy, AI education implementation, teacher preparation, assessment, and responsible AI learning.

• Conceptual understanding
• Engagement / motivation
• Teacher readiness
• Curriculum / course design
• Learning tool / resource design
• Teacher professional development
• Assessment support
• Outreach / informal learning

• Completed

K-12, Higher education

In-school (K-12), Higher education, Informal learning

Robotics / physical AI, Explainable AI / robustness, Assessment / tutoring analytics

Instructional / curriculum-based learning

Low to Medium

Medium

Using Explainable AI and Hierarchical Planning for Outreach with Robots

• Rushang Karia
• Jayesh Nagpal
• Daksh Dobhal
• Pulkit Verma
• Rashmeet Kaur Nayyar
• Naman Shah
• Siddharth Srivastava

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 39 No. 28, EAAI-25

2025

10.1609/aaai.v39i28.35172

https://ojs.aaai.org/index.php/AAAI/article/view/35172

https://ojs.aaai.org/index.php/AAAI/article/view/35172/37327

009_Using Explainable AI and Hierarchical Planning for Outreach with Robots.pdf

9

Understanding how robots plan and execute tasks is crucial in today’s world, where they are becoming more prevalent in our daily lives. However, teaching non-experts, such as K-12 students, the complexities of robot planning can be challeng- ing. This work presents an open-source platform, JEDAI.Ed, that simplifies the process using a visual interface that ab- stracts the details of various planning processes that robots use for performing complex mobile manipulation tasks. Us- ing principles developed in the field of explainable AI, this in- tuitive platform enables students to use a high-level intuitive instruction set to perform complex tasks, visualize them on an in-built simulator, and to obtain helpful hints and natural language explanations for errors. Finally, JEDAI.Ed, includes an adaptive curriculum generation method that provides stu- dents with customized learning ramps. This platform’s effi- cacy was tested through a user study with university students who had little to no computer science background. Our results show that JEDAI.Ed is highly effective in increasing student engagement, teaching robotics programming, and decreasing the time need to solve tasks as compared to baselines. 1 Motivation Recent advances in Artificial Intelligence (AI) have enabled the deployment of programmable AI robots that can assist humans in a myriad of tasks. However, such advances will have limited utility and scope if users need to have advanced technical knowledge to use them safely and productively. For instance, a mechanical arm robot that can assist humans in assembling different types of components will have lim- ited utility if the operator is unable to understand what it can do, and cannot effectively re-task it to help with new designs. This paper aims to develop new methods that will allow educators and AI system manufacturers to introduce users to AI systems on the fly, i.e., without requiring advanced de- grees in Computer Science/AI as prerequisites. These meth- ods allow for introducing robotics programming to novices. Our contribution We accomplish our overall objective by introducing JEDAI.Ed, a web application that abstracts the intricacies of robotics programming and exposes the user *These authors contributed equally.

• In-school (K-12)
• Higher education
• Informal learning

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

Not specified in extracted text unless noted in duration field.

Requires educators/researchers/facilitators with sufficient AI literacy and pedagogy knowledge for the target learners.

Infrastructure depends on AI tool type, learner devices, data access, and institutional policy context.

High

• duration

This entry was automatically extracted from the PDF text and manifest metadata. Fields should be manually verified before public registry publication, especially group size, location, duration, and outcome claims.