Guiding Students to Investigate What Google Speech Recognition Knows about Language

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)

Classroom, course, or resource-based AI education activity

Not specified in extracted text

speech recognition systems but are largely unaware of how they work. Teaching K-12 students to investigate how these systems employ phonolog- ical, syntactic, semantic, and cultural knowledge to resolve ambiguit; question answering. Introduction This paper presents an approach to teaching K-12 students about speech recognition and the nature of language. Al- though most children today have experienced or at least ob- se; alongside the original, and then speaks the translation. Another place where K-12 students can encounter speech to text software is in AI extensions to children’s pro- gramming languages. Both MachineLearningFo

Computer vision / image classification, NLP / text classification

• Use with minors requires attention to privacy, consent, data minimization, and adult supervision.

6-8

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.
• Children of all ages interact with speech recognition systems but are largely unaware of how they work.

• 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.

Computer vision / image classification, NLP / text classification

Language context discussed in source publication

• Learning object / concept model

• Primary interaction pattern inferred from publication: Outreach / informal learning, Ethics / responsible AI education.
• AI capability focus: Computer vision / image classification, NLP / text classification.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.
• 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.

• 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.
• Teaching K-12 students to investigate how these systems employ phonolog- ical, syntactic, semantic, and cultural knowledge to resolve ambiguities in the audio signal can provide them a window on complex AI decision-making and also help them appre- ciate the richness and complexity of human language.

• Teaching K-12 students to investigate how these systems employ phonolog- ical, syntactic, semantic, and cultural knowledge to resolve ambiguities in the audio signal can provide them a window on complex AI decision-making and also help them appre- ciate the richness and complexity of human language.

Children of all ages interact with speech recognition systems but are largely unaware of how they work. Teaching K-12 students to investigate how these systems employ phonolog- ical, syntactic, semantic, and cultural knowledge to resolve ambiguities in the audio signal can provide them a window on complex AI decision-making and also help them appre- ciate the richness and complexity of human language.

• Use age-appropriate framing and teacher/facilitator oversight for any classroom deployment.
• Minimize personal data collection and avoid storing identifiable learner media unless approved by local policy/IRB.
• Include bias, fairness, transparency, and social impact discussion as part of the learning design.

• Pre/post or experimental 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
• Ethics and responsible use
• Outreach / informal learning
• Ethics / responsible AI education
• Computer vision / image classification
• NLP / text classification

• Completed

6-8

In-school (K-12)

Computer vision / image classification, NLP / text classification

Instructional / curriculum-based learning

Medium

High

Guiding Students to Investigate What Google Speech Recognition Knows about Language

• David S. Touretzky
• Christina Gardner-McCune

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

2023

10.1609/aaai.v37i13.26905

https://ojs.aaai.org/index.php/AAAI/article/view/26905

https://ojs.aaai.org/index.php/AAAI/article/view/26905/26677

096_Guiding Students to Investigate What Google Speech Recognition Knows about Language.pdf

8

Children of all ages interact with speech recognition systems but are largely unaware of how they work. Teaching K-12 students to investigate how these systems employ phonolog- ical, syntactic, semantic, and cultural knowledge to resolve ambiguities in the audio signal can provide them a window on complex AI decision-making and also help them appre- ciate the richness and complexity of human language. We describe a browser-based tool for exploring the Google Web Speech API and a series of experiments students can engage in to measure what the service knows about language and the types of biases it exhibits. Middle school students taking an introductory AI elective were able to use the tool to explore Google’s knowledge of homophones and its ability to exploit context to disambiguate them. Older students could poten- tially conduct more comprehensive investigations, which we lay out here. This approach to investigating the power and limitations of speech technology through carefully designed experiments can also be applied to other AI application areas, such as face detection, object recognition, machine transla- tion, or question answering.

• In-school (K-12)

• 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.