mlsanityData to 7 Oct 2026

If I Hear You Correctly: Building and Evaluating Interview Chatbots with Active Listening Skills

arXiv 2002.01862 ↗First posted 5 Feb 2020cs.HC cs.AI cs.CLcited by 20

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Abstract (arXiv)

Interview chatbots engage users in a text-based conversation to draw out their views and opinions. It is, however, challenging to build effective interview chatbots that can handle user free-text responses to open-ended questions and deliver engaging user experience. As the first step, we are investigating the feasibility and effectiveness of using publicly available, practical AI technologies to build effective interview chatbots. To demonstrate feasibility, we built a prototype scoped to enable interview chatbots with a subset of active listening skills - the abilities to comprehend a user's input and respond properly. To evaluate the effectiveness of our prototype, we compared the performance of interview chatbots with or without active listening skills on four common interview topics in a live evaluation with 206 users. Our work presents practical design implications for building effective interview chatbots, hybrid chatbot platforms, and empathetic chatbots beyond interview tasks.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Feb 2020.

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Citing papers per year: 2021: 1; 2022: 3; 2023: 4; 2024: 3; 2025: 2; 2026: 7.

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  1. 2023Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Datacited by 48
  2. 2022Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generationcited by 47
  3. 2022What should I Ask: A Knowledge-driven Approach for Follow-up Questions Generation in Conversational Surveyscited by 13
  4. 2023On the Effectiveness of Creating Conversational Agent Personalities Through Promptingcited by 10
  5. 2022Human-Centric Research for NLP: Towards a Definition and Guiding Questionscited by 9
  6. 2023Comparing How a Chatbot References User Utterances from Previous Chatting Sessions: An Investigation of Users' Privacy Concerns and Perceptionscited by 8
  7. 2024A Novel Nuanced Conversation Evaluation Framework for Large Language Models in Mental Healthcited by 7
  8. 2024Understanding Communication Preferences of Information Workers in Engagement with Text-Based Conversational Agentscited by 6
  9. 2023Inform the uninformed: Improving Online Informed Consent Reading with an AI-Powered Chatbotcited by 5
  10. 2025AI-Enabled Conversational Journaling for Advancing Parkinson's Disease Symptom Trackingcited by 4
  11. 2026Feedback by Design: Understanding and Overcoming User Feedback Barriers in Conversational Agentscited by 4
  12. 2026Hear You in Silence: Designing for Active Listening in Human Interaction with Conversational Agents Using Context-Aware Pacingcited by 3
8 more
  1. 2024Exploring the Human-LLM Synergy in Advancing Theory-driven Qualitative Analysiscited by 2
  2. 2025Interview AI-ssistant: Designing for Real-Time Human-AI Collaboration in Interview Preparation and Executioncited by 2
  3. 2026The Bots of Persuasion: Examining How Conversational Agents' Linguistic Expressions of Personality Affect User Perceptions and Decisionscited by 2
  4. 2026Synthetic Users, Real Differences: an Evaluation Framework for User Simulation in Multi-Turn Conversationscited by 2
  5. 2021Towards Automated Psychotherapy via Language Modelingcited by 1
  6. 2026Chaplains' Reflections on the Design and Usage of AI for Conversational Carecited by 1
  7. 2026AInterviewer: A Platform for Designing and Conducting AI-led Qualitative Interviewscited by 0
  8. 2026DeepSAGE: Stage-Aware Reinforcement Learning for Structured CBT Counseling Dialoguecited by 0