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Dergiler / Turkish Journal of Engineering / 2026 / Volume: 10 Issue: 1

Designing Emotionally Adaptive Chatbots for Diverse Users: A User-Centered Human-AI Interface Framework

Priyanka Deshmukh(G H Raisoni University)0009-0006-6907-3242
Bhavana Karmore(G H Raisoni University)0009-0004-0652-3037
Mahendra Ingole(Chhatrapati Shivaji Maharaj University)0000-0003-1219-524X
Kamal Upreti(CHRIST(Deemed to be University))0000-0003-0665-530X
Sayfa
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Yayın Tarihi
2025-12-16
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Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The framework is integration-ready with NLP engines like GPT and Dialogflow, offering a scalable solution for affective AI deployment across healthcare, education, and public service domains.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Recent advancements in conversational AI have improved task efficiency but often neglect the emotional and cognitive diversity of users.
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Temel Bulgular & Veri:This research introduces a novel, user-centered framework for emotionally adaptive chatbots that integrates ML-based emotion recognition with personalized responses that are ethically filtered — meaning they are designed to respect user privacy, fairness, and transparency principles.
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Sonuç & Katkı:The framework is integration-ready with NLP engines like GPT and Dialogflow, offering a scalable solution for affective AI deployment across healthcare, education, and public service domains.
🔬Tür:Uygulamalı Anket & Alan Araştırması
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Remote sensing · UAV · Photogrammetry

Abstract

Recent advancements in conversational AI have improved task efficiency but often neglect the emotional and cognitive diversity of users. This research introduces a novel, user-centered framework for emotionally adaptive chatbots that integrates ML-based emotion recognition with personalized responses that are ethically filtered — meaning they are designed to respect user privacy, fairness, and transparency principles. The Berlin Emotional Speech Database (EmoDB) was used to train and evaluate three machine learning models using MFCC features. Among them, the XGBoost model achieved the highest classification accuracy of 77.6%, outperforming Random Forest (75.0%) and SVM (68.2%). To evaluate user experience, a dataset of 385 participants was generated using a 15-item Likert-scale questionnaire adapted from the UTAUT model and extended with trust and emotional alignment measures. Statistical tests, including a t-test (p = 0.711) between neurodiverse and non-neurodiverse users and an ANOVA (p = 0.337) across domains, confirmed the consistency and inclusivity of perceived satisfaction. Visual analytics, including correlation heatmaps and radar charts, revealed that users with predicted emotions such as happiness and neutral reported the highest satisfaction scores (mean = 4.49, SD = 0.29 and mean = 4.26, SD = 0.31, respectively). A seven-layered modular architecture was proposed, supporting real-time emotional adaptivity, personalization, and ethical compliance. The framework is integration-ready with NLP engines like GPT and Dialogflow, offering a scalable solution for affective AI deployment across healthcare, education, and public service domains.

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Bu Makaleye Atıf Yap

Deshmukh, P., Karmore, B., Ingole, M., Upreti, K. (2025). Designing Emotionally Adaptive Chatbots for Diverse Users: A User-Centered Human-AI Interface Framework. Turkish Journal of Engineering, Volume: 10 Issue: 1. https://doi.org/10.31127/tuje.1715271

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