Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture

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)

Course implementation or course design

Not specified in extracted text

students should know about AI based on consultations with both AI experts and K-12 educators. These themes are broad guidelines for AI educa- tion and include ”Perception”, ”Representation and Reason- ing”, ”Lear

LLM/Chat, ML concepts / supervised learning, Explainable AI / robustness, Ethics / responsible AI

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

K-5

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.
• As artificial intelligence (AI) becomes a prominent part of modern life, AI literacy is becoming important for all citi- zens, not just those in technology careers.

• 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.

LLM/Chat, ML concepts / supervised learning, Explainable AI / robustness, Ethics / responsible AI

Language context discussed in source publication

• Tutor
• Evaluator

• Primary interaction pattern inferred from publication: Curriculum / course design, Learning tool / resource design, Teacher professional development, Ethics / responsible AI education.
• AI capability focus: LLM/Chat, ML concepts / supervised learning, Explainable AI / robustness, Ethics / responsible AI.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• 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.
• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

• Case classified under: Published empirical study.
• 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.
• Previous research in AI education materials has largely focused on the intro- duction of terminology as well as AI use cases and ethics, but few allow students to learn by creating their own machine learning models.

• Previous research in AI education materials has largely focused on the intro- duction of terminology as well as AI use cases and ethics, but few allow students to learn by creating their own machine learning models.
• As such, we propose the development of an open-source tool (Build-a-Bot) for stu- dents and teachers to not only create their own transformer- based chatbots based on their own course material, but also learn the fundamentals of AI through the model creation pro- cess.
• The primary concern of this paper is the creation of an interface for students to learn the principles of artificial intelligence by using a natural language pipeline to train a customized model to answer questions based on their own school curriculums.

As artificial intelligence (AI) becomes a prominent part of modern life, AI literacy is becoming important for all citi- zens, not just those in technology careers. Previous research in AI education materials has largely focused on the intro- duction of terminology as well as AI use cases and ethics, but few allow students to learn by creating their own machine learning models.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Require human review of generated outputs and explicit guidance against over-reliance or answer copying.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Activity documentation

• 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.

• AI literacy
• Conceptual understanding
• Engagement / motivation
• Ethics and responsible use
• Teacher readiness
• Curriculum / course design
• Learning tool / resource design
• Teacher professional development

• Completed

K-5

In-school (K-12)

LLM/Chat, ML concepts / supervised learning, Explainable AI / robustness, Ethics / responsible AI

Instructional / curriculum-based learning

Medium

Medium

Build-a-Bot: Teaching Conversational AI Using a Transformer-Based Intent Recognition and Question Answering Architecture

• Kate Pearce
• Sharifa Alghowinem
• Cynthia Breazeal

Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 37 No. 13, EAAI-23

2023

10.1609/aaai.v37i13.26903

https://ojs.aaai.org/index.php/AAAI/article/view/26903

https://ojs.aaai.org/index.php/AAAI/article/view/26903/26675

094_Build-a-Bot_ Teaching Conversational AI Using a Transformer-Based Intent Recognition and QA Architecture.pdf

8

As artificial intelligence (AI) becomes a prominent part of modern life, AI literacy is becoming important for all citi- zens, not just those in technology careers. Previous research in AI education materials has largely focused on the intro- duction of terminology as well as AI use cases and ethics, but few allow students to learn by creating their own machine learning models. Therefore, there is a need for enriching AI educational tools with more adaptable and flexible platforms for interested educators with any level of technical experience to utilize within their teaching material. As such, we propose the development of an open-source tool (Build-a-Bot) for stu- dents and teachers to not only create their own transformer- based chatbots based on their own course material, but also learn the fundamentals of AI through the model creation pro- cess. The primary concern of this paper is the creation of an interface for students to learn the principles of artificial intelligence by using a natural language pipeline to train a customized model to answer questions based on their own school curriculums. The model uses contexts given by their instructor, such as chapters of a textbook, to answer questions and is deployed on an interactive chatbot/voice agent. The pipeline teaches students data collection, data augmentation, intent recognition, and question answering by having them work through each of these processes while creating their AI agent, diverging from previous chatbot work where students and teachers use the bots as black-boxes with no abilities for customization or the bots lack AI capabilities, with the major- ity of dialogue scripts being rule-based. In addition, our tool is designed to make each step of this pipeline intuitive for stu- dents at a middle-school level. Further work primarily lies in providing our tool to schools and seeking student and teacher evaluations.

• In-school (K-12)

• Teacher readiness, time, support, and classroom integration may affect implementation quality.
• AI output reliability, hallucination, academic integrity, and age-appropriate use require safeguards.
• 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.