2023Wang et al., Emory University
InterviewBot: End-to-End Dialogue Systems
→ Implemented: Sliding Window & Context Attention deployed
View Paper ↗Standing on the shoulders of giants
We study peer-reviewed breakthroughs from Stanford, KAIST, CMU, Emory, and micro1 to inform how we model school interview preparation systems. Every capability we discuss is grounded in published science rather than proprietary performance claims.
Implement frameworks proven in academic research and reproduce their findings in production settings.
Benchmark against peer-reviewed datasets before any feature ships to enterprise customers.
Monitor thousands of interviews for drift, variance, and emerging best practices.
Map every capability to its research foundation, making due diligence effortless for stakeholders.
Each pillar below links to a deep dive detailing the academic origin, how we incorporate the research into modeling decisions, and the outcomes reported by the original authors.
The academic foundation
Traditional single-turn evaluation misses critical behaviors. Kim et al. (KAIST/CMU/Stanford, 2025) introduced the LLM-as-an-Interviewer paradigm to capture adaptability, feedback incorporation, and follow-up proficiency while mitigating data contamination.
How we use this research in modeling
We reference this framework when modeling 20-30 minute adaptive interviews, aligning question design and reporting structures with the study's multi-turn approach.
Published takeaways
The academic foundation
Allbert et al. (micro1, 2025) compared STT × LLM × TTS stacks over 300K interviews and demonstrated STT quality dominates performance, LLM choice shapes satisfaction, and TTS impacts perceived experience.
How we use this research in modeling
We compare stack options against the published findings to anticipate how transcription, reasoning, and synthesis choices may affect client experiences.
Published takeaways
The academic foundation
Ege & Ceyhan (Huawei Turkey, 2023) pioneered privacy-preserving cheating detection that runs entirely on the candidate device, achieving 97.1% voice classification accuracy with BodyPix segmentation for compliance.
How we use this research in modeling
We adopt the client-side design principles from the study as guardrails when modeling privacy-preserving monitoring.
Published takeaways
The academic foundation
Wang et al. (Emory/InitialView, 2023) introduced sliding windows, context attention, and topic storing to extend transformer memory—cutting topic repetition to 6.7% and quadrupling interview completion rates.
How we use this research in modeling
We draw on these mechanisms when shaping long-context policies for our modeling experiments and runtime safeguards.
Published takeaways
We study academic benchmarks alongside anonymized interview datasets to understand how published findings might translate into client-facing scenarios.
| Metric | Baseline | Published Result | Source |
|---|---|---|---|
| Speaker Diarization (F1) | 72.7% | 93.6% | Wang et al., 2023 |
| Topic Repetition Rate | 30.0% | 6.7% | Wang et al., 2023 |
| Interview Completion | 13.3% | 46.7% | Wang et al., 2023 |
| Off-Topic Responses | 20.0% | 10.0% | Wang et al., 2023 |
| User Satisfaction (1-5) | 4.29 | 4.53 | Allbert et al., 2025 |
| Conversational Quality (1-10) | 8.32 | 8.78 | Allbert et al., 2025 |
Translating academic findings into production practice keeps our platform accurate, transparent, and fair.
Research finding
Kim et al. (2025) observed 57-84 point drops when contaminated test items were paraphrased, proving static benchmarks overstate capability.
Our application
Quarterly refreshed question banks, automated paraphrasing, and contamination sweeps against public corpora maintain evaluation integrity.
Research finding
Multiple studies highlight verbosity and self-enhancement bias in LLM judges, along with high variance across single-run evaluations.
Our application
We design multi-turn scoring pipelines that pursue the low length correlation (≈0.013) reported in research, combining ensemble judge models with human audits to keep scores trustworthy.
Research finding
Allbert et al. (2025) found weak correlation (r<0.11) between automated scores and user satisfaction, underscoring the need for qualitative signals.
Our application
We pair quantitative metrics with interviewer feedback loops, pacing adjustments, and voice UX tuning tailored to each industry.
We monitor new publications, evaluate emerging models, and feed live telemetry back into our roadmap to stay on the scientific frontier.
→ Implemented: Sliding Window & Context Attention deployed
View Paper ↗→ Implemented: Client-side proctoring launched
View Paper ↗→ Implemented: Dynamic multi-turn evaluation operationalized
View Paper ↗→ Implemented: Voice stack optimization complete
View Paper ↗Share research summaries, architecture diagrams, and benchmark comparisons with procurement, legal, or technical reviewers.
Access executive summaries, technical documentation, and benchmark comparisons to share with stakeholders.
How SchoolsAdmissions applies frontier research to school interview preparation.
Download PDFArchitecture diagrams, evaluation protocols, and deployment considerations.
Download PDFDiscover how published research informs the interview experiences we model and tailor for clients.