Detecting Exclusive Language during Pair Programming

Source publication / research team or educational organization described in paper

USA

Researchers, educators, instructors, or facilitators as described in the source publication

• Research / curriculum design context

Tool / platform-supported learning activity

Not specified in extracted text

40,490 labeled utterances obtained from three programming assignments involving 34 students pair programming in a remote environment. This research involves the first successful automated detection of exclusive; e graduate course. Data Collection and Participants The data was collected from 34 participants who were all graduate students at a large university in the United States and had prior programming experience. The stu; Tuning, and Testing Datasets We partitioned the data by student, splitting the 34 students randomly. The training dataset contains the utterances of 20 students, the tuning dataset contains the utterances of 7

NLP / text classification, ML concepts / supervised learning

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

Unspecified / broad education

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.
• Inclusive team participation is one of the most important factors that aids effective collaboration and pair programming.

• 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.

NLP / text classification, ML concepts / supervised learning

Language context discussed in source publication

• Learning object / concept model

• Primary interaction pattern inferred from publication: Curriculum / course design, Learning tool / resource design.
• AI capability focus: NLP / text classification, ML concepts / supervised learning.

• Apply standard AAB safeguards: privacy, transparency, human oversight, and documentation of limitations.

• 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.

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

• Case classified under: Published curriculum / implementation paper.
• 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.
• In this paper, we investigated the ability of linguistic features and a transformer-based language model to detect exclusive and inclusive language.

• In this paper, we investigated the ability of linguistic features and a transformer-based language model to detect exclusive and inclusive language.

Inclusive team participation is one of the most important factors that aids effective collaboration and pair programming. In this paper, we investigated the ability of linguistic features and a transformer-based language model to detect exclusive and inclusive language.

• Apply standard AAB safeguards: privacy, transparency, human oversight, and documentation of limitations.

• Design / conceptual 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.

• Implementation guidance
• Curriculum / course design
• Learning tool / resource design
• NLP / text classification
• ML concepts / supervised learning

• Completed

Unspecified / broad education

Research / curriculum design context

NLP / text classification, ML concepts / supervised learning

Instructional / curriculum-based learning

Medium

Medium

Detecting Exclusive Language during Pair Programming

• Solomon Ubani
• Rodney Nielsen
• Helen Li

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

2023

10.1609/aaai.v37i13.26895

https://ojs.aaai.org/index.php/AAAI/article/view/26895

https://ojs.aaai.org/index.php/AAAI/article/view/26895/26667

086_Detecting Exclusive Language during Pair Programming.pdf

8

Inclusive team participation is one of the most important factors that aids effective collaboration and pair programming. In this paper, we investigated the ability of linguistic features and a transformer-based language model to detect exclusive and inclusive language. The task of detecting exclusive language was approached as a text classification problem. We created a research community resource consisting of a dataset of 40,490 labeled utterances obtained from three programming assignments involving 34 students pair programming in a remote environment. This research involves the first successful automated detection of exclusive language during pair programming. Additionally, this is the first work to perform a computational linguistic analysis on the verbal interaction common in the context of inclusive and exclusive language during pair programming.

• Research / curriculum design context

• The paper provides limited implementation detail in the extracted abstract; additional manual review may be needed for local replication.

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.