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Jayesh Nagpal

dblp:371/1144 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Interdisciplinary, comprehensive, and emerging computing
1 paper
Computing education · 100%
Human-computer interaction and pervasive computing
1 paper
Human-robot interaction · 100%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computing education › robotics education
educational robotics platform
0.912025
Using Explainable AI and Hierarchical Planning for Outreach with Robots · AAAI 2025
Computing education
robotics education
0.912025
Using Explainable AI and Hierarchical Planning for Outreach with Robots · AAAI 2025

Methods — techniques the papers use, named apart from their topics

hierarchical planning · 1.7explainable AI · 1.7
YearPublicationVenuePosition
2025 Using Explainable AI and Hierarchical Planning for Outreach with Robots
abstract
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 challenging. This work presents an open-source platform, JEDAI.Ed, that simplifies the process using a visual interface that abstracts the details of various planning processes that robots use for performing complex mobile manipulation tasks. Using principles developed in the field of explainable AI, this intuitive 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 students with customized learning ramps. This platform's efficacy 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.
Rushang Karia, Jayesh Nagpal, Daksh Dobhal, Pulkit Verma 0001, Rashmeet Kaur Nayyar, Naman Shah 0002, Siddharth Srivastava 0001
AAAI2