IRobot: Teaching the Basics of Artificial Intelligence in High Schools

Authors: Harald Burgsteiner, Martin Kandlhofer, Gerald Steinbauer

AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

Reproducibility Variable Result LLM Response
Research Type Experimental A pilot project was conducted and empirically evaluated. Results of the evaluation show that the participating pupils have become familiar with those concepts and the various topics addressed. The project evaluation was done using reliable qualitative and quantitative empirical research methods (Diekmann 2007).
Researcher Affiliation Academia Harald Burgsteiner Institute for e Health Graz University of Applied Sciences Eggenberger Allee 11, 8020 Graz, Austria harald.burgsteiner@fh-joanneum.at Martin Kandlhofer Institute for Software Technology Graz University of Technology Inffeldgasse 16b/II, 8010 Graz, Austria kandlhofer@ist.tugraz.at Gerald Steinbauer Institute for Software Technology Graz University of Technology Inffeldgasse 16b/II, 8010 Graz, Austria steinbauer@ist.tugraz.at
Pseudocode No The paper describes the conceptual aspects of algorithms like A* search but does not provide pseudocode or explicit algorithm blocks.
Open Source Code No The paper does not provide any explicit statement or link regarding the open-source code for the described methodology.
Open Datasets No Questionnaire, interview guiding questions and data are available upon request.
Dataset Splits No The evaluation describes data collection methods (questionnaires, interviews, field notes) from 9 pupils but does not involve standard dataset splits (train/validation/test) for model training or evaluation.
Hardware Specification No The paper mentions 'building Braitenberg vehicles using Lego Mindstorms NXT' as part of the course activities, but it does not specify any hardware used for data analysis or computational experiments in the context of research results.
Software Dependencies No The paper mentions 'implementing the A* algorithm in C#' but does not provide specific version numbers for software dependencies or libraries used for the experiments or analysis.
Experiment Setup No The paper describes the structure and content of the AI course and its evaluation, but it does not provide specific experimental setup details such as hyperparameter values or training configurations typically found in computational research.