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Virginia Tech

Towards Human-AI Teaming for Skill Development: From Dyadic Interview Practice to Triadic Programming Collaboration

Abstract

dc:description.abstract

As the job market for CS graduates grows increasingly competitive, effective preparation for technical interviews through mock interviews and programming practice has become critical. This thesis explores how AI can support skill development in these domains, progressing from AI as a sole practice partner to AI as a collaborative teammate. Our first two studies investigate LLM-based conversational AI for interview preparation. Study 1 developed an interview system grounded in reflective learning and dialogic feedback, enabling learners to engage in low-stakes practice with personalized, interactive feedback. Study 2 extended this work to technical interviews, exploring how conversational AI can support the think-aloud practice in technical interviews through simulation, feedback, and example. Together, these studies demonstrate the value of AI as a dyadic practice partner. However, participants reported that while AI practice was useful, peer-based engagement may offer stronger social connection and motivation, suggesting that AI should augment rather than replace human collaboration. This insight motivated our third study, which investigates human–AI teaming in a triadic configuration. We introduce human–human–AI triadic programming, where two humans collaborate with a proactive AI agent. Results from 20 participants show that this triadic collaboration improves collaborative learning and social presence while encouraging more responsible AI use. Together, these studies advance understanding of how AI can support skill development, illustrating a trajectory from dyadic human–AI interaction toward richer forms of human–AI teaming that preserve the pedagogical and social benefits of human collaboration.

Degree

thesis:*
Name thesis:degree_name
Master of Science
Level thesis:degree_level
masters
Discipline thesis:degree_discipline
Computer Science & Applications
Department dc:contributor.department
Computer Science and#38; Applications
Grantor dc:publisher
Virginia Tech
Year dc:date.issued
2026

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Daryanto, Taufiq Husada
Chair dc:contributor.committeechair
  • Rho, Ha Rim
Committee members dc:contributor.committeemember
  • Brown, Dwayne Christian
  • Chen, Yan

Subjects

dc:subject × 3

Rights

dc:rights
Statement dc:rights
  • In Copyright
Language dc:language.iso
en

Identifiers

dc:identifier.*
Dc Identifier Other
vt_gsexam:45422
OAI identifier oai:identifier
oai:vtechworks.lib.vt.edu:10919/140614

Chain of custody

source
Harvested from
Virginia Tech
Base URL
vtechworks.lib.vt.edu/oai/request
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Daryanto, Taufiq Husada. Towards Human-AI Teaming for Skill Development: From Dyadic Interview Practice to Triadic Programming Collaboration. masters thesis, Virginia Tech, 2026. https://hdl.handle.net/10919/140614