short-paper

A Chatbot-Server Framework for Scalable Machine Learning Education through Crowdsourced Data

Authors:

Jingting Li,

Chee Wei Tan,

Ching Nam Hang,

Xintong QiAuthors Info & Claims

L@S '22: Proceedings of the Ninth ACM Conference on Learning @ Scale

Pages 271 - 274

https://doi.org/10.1145/3491140.3528300

Published: 01 June 2022 Publication History

Get Access

Abstract

In this paper, we propose a novel chatbot-server computer programming framework for students to learn Artificial Intelligence (AI) by creating game AI chatbot applications, whilst conforming to a distributed frontend-backend application structure (e.g., client-server model). The chatbot interface allows students to share their work over online social networks and invite other human players to test-drive the game AI and to collect data for training of machine learning models by crowdsourcing. We introduce a few test cases in which the framework facilitates the online learning of AI, introduces full-stack software development to students and enables a progressive learning of machine learning education using crowdsourcing.

Supplemental Material

MP4 File

In this video, we present the paper "A Chatbot-Server Framework for Scalable Machine Learning Education through Crowdsourced Data." With the framework, students are able to learn artificial intelligence and machine learning by writing their own games empowered by AI. The system consists of two core components: the chatbot interface and the server. We introduce their use cases respectively. Besides, we showcase our framework with a case study of the Nim game. The Nim game is a two-player game where players take turns removing stones from the initial pile. Last but not least, the performance of the Nim game AI under different game configurations is also analyzed.

Download
17.25 MB

References

[1]

Lewis Carroll. 1958. Symbolic logic and the game of logic. Vol. 1. Courier Corporation, New York.

Google Scholar

[2]

Gary Kacmarcik. 2006. Using natural language to manage NPC dialog. In Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, Vol. 2. 115--117.

Google Scholar

[3]

Lin Ling and Chee Wei Tan. 2018. Human-Assisted Computation for Auto-grading. In IEEE ICDM Workshop on Machine Learning for Education.

Google Scholar

[4]

Donald Michie. 1963. Experiments on the mechanization of game-learning Part I: Characterization of the model and its parameters. In The Computer Journal, Vol. 6. 232--236.

Crossref

Google Scholar

[5]

Daniel Smilkov, Shan Carter, D Sculley, Fernanda B Viégas, and Martin Wattenberg. 2017. Direct-manipulation visualization of deep networks. arXiv preprint arXiv:1708.03788 (2017).

Google Scholar

[6]

David G. Stork. 2000. Open data collection for training intelligent software in the Open Mind Initiative. In Proceedings of the Engineering Intelligent Systems.

Google Scholar

[7]

David Touretzky, Christina Gardner-McCune, Fred Martin, and Deborah Seehorn. 2019. Envisioning AI for K-12: What Should Every Child Know about AI?. In The Thirty-Third AAAI Conference on Artificial Intelligence.

Digital Library

Google Scholar

[8]

Luis von Ahn and Laura Dabbish. 2008. Designing games with a purpose. In Communications of the ACM, Vol. 51. 57--67.

Digital Library

Google Scholar

[9]

Hao Wang. 1984. Computer theorem proving and artificial intelligence. In Automated Theorem Proving: After 25 Years, American Mathematical Society, Contemporary Mathematics, Vol. 29. Springer, Dordrecht, The Netherlands, 49--70.

Google Scholar

Cited By

View all

Rashik MJasim MKucher KSarvghad AMahyar N(2024)Beyond Text and Speech in Conversational Agents: Mapping the Design Space of AvatarsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661563(1875-1894)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661563
Hang CWei Tan CYu P(2024)MCQGen: A Large Language Model-Driven MCQ Generator for Personalized LearningIEEE Access10.1109/ACCESS.2024.342070912(102261-102273)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3420709
Magoo CSingh M(2023)A Proposed Architecture of Mobile Service Provider Based Interactive Systems2023 IEEE 8th International Conference for Convergence in Technology (I2CT)10.1109/I2CT57861.2023.10126465(1-4)Online publication date: 7-Apr-2023
https://doi.org/10.1109/I2CT57861.2023.10126465

Index Terms

A Chatbot-Server Framework for Scalable Machine Learning Education through Crowdsourced Data

Recommendations

Mind the Gap: Confronting the Vast Divide Between CS Teaching and Machine Learning Pedagogy
Technology Enhanced Learning for Inclusive and Equitable Quality Education
Abstract
The increasing demand for machine learning and deep learning (ML/DL) education spans K-12, college, and vocational training, highlighting the urgent need to prepare Computer Science (CS) teachers for these fields. This study aims to characterize ...
Teaching Machine Learning in K-12 Education
ICER 2021: Proceedings of the 17th ACM Conference on International Computing Education Research

This research is interested in how to teach machine learning concepts to K-12 learners. There is limited evidence to support the teaching, learning, and usefulness of machine learning in K-12 settings, hence addressing the evident gap. This research ...
Unplugged Decision Tree Learning – A Learning Activity for Machine Learning Education in K-12
Creative Mathematical Sciences Communication
Abstract
Artificial intelligence (AI) is now deeply ingrained in young peoples’ everyday lives. They need low-threshold learning opportunities to understand what AI is and how it works. Unplugged learning activities can offer such opportunities but must ...

Comments

Information & Contributors

Information

Published In

L@S '22: Proceedings of the Ninth ACM Conference on Learning @ Scale

June 2022

491 pages

ISBN:9781450391580

DOI:10.1145/3491140

General Chair:
René F. Kizilcec
Cornell University, USA
,
Program Chairs:
Katie Davis
University of Washington, USA
,
Xavier Ochoa
New York University, USA

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 June 2022

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Short-paper

Conference

L@S '22

L@S '22: Ninth (2022) ACM Conference on Learning @ Scale

June 1 - 3, 2022

NY, New York City, USA

Acceptance Rates

Overall Acceptance Rate 117 of 440 submissions, 27%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

3
Total Citations
View Citations
273
Total Downloads

Downloads (Last 12 months)38
Downloads (Last 6 weeks)3

Reflects downloads up to 03 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

Rashik MJasim MKucher KSarvghad AMahyar N(2024)Beyond Text and Speech in Conversational Agents: Mapping the Design Space of AvatarsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661563(1875-1894)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661563
Hang CWei Tan CYu P(2024)MCQGen: A Large Language Model-Driven MCQ Generator for Personalized LearningIEEE Access10.1109/ACCESS.2024.342070912(102261-102273)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3420709
Magoo CSingh M(2023)A Proposed Architecture of Mobile Service Provider Based Interactive Systems2023 IEEE 8th International Conference for Convergence in Technology (I2CT)10.1109/I2CT57861.2023.10126465(1-4)Online publication date: 7-Apr-2023
https://doi.org/10.1109/I2CT57861.2023.10126465

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Abstract

Supplemental Material

References

Cited By

Index Terms

Recommendations

Mind the Gap: Confronting the Vast Divide Between CS Teaching and Machine Learning Pedagogy

Teaching Machine Learning in K-12 Education

Unplugged Decision Tree Learning – A Learning Activity for Machine Learning Education in K-12

Comments

Information

Published In

Publisher

Publication History

Permissions

Check for updates

Author Tags

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Login options

Full Access

View options

PDF

eReader

Share

Share this Publication link

Share on social media

Affiliations