<?xml version="1.0" encoding="UTF-8" standalone="yes"?><?xml-stylesheet type="text/xsl" href="/oai-pmh-repository/static/oai2.xsl"?>
<OAI-PMH xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/ http://www.openarchives.org/OAI/2.0/OAI-PMH.xsd">
    <responseDate>2026-10-16T00:51:07Z</responseDate>
    <request verb="GetRecord" identifier="oai:data.mendeley.com/54tbst239t.1" metadataPrefix="oai_dc">https://data.mendeley.com/oai</request>
    <GetRecord>
        <record>
            <header>
                <identifier>oai:data.mendeley.com/54tbst239t.1</identifier>
                <datestamp>2026-06-11T05:13:38Z</datestamp>
            </header>
            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator>Hossain, Md Shamim</dc:creator>
    <dc:title>AI Chatbot Engagement Dataset</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>This dataset contains anonymised, processed user reviews from the Google Play Store for two AI conversational agents, ChatGPT and DeepSeek. Only reviews containing education‑related keywords (e.g., homework, study, exam, student, teacher) were retained, resulting in a final sample of 83,752 English‑language reviews after text cleaning, removal of reviews shorter than 5 words, and language detection.

Variables
Column	Description
score	User rating (1 to 5 stars)
App	Platform identifier: ChatGPT or DeepSeek
cognitive	Binary indicator (1 = review mentions explanation quality, step‑by‑step reasoning, accuracy, etc.)
affective	Binary indicator (1 = review mentions encouragement, patience, supportive tone, etc.)
social	Binary indicator (1 = review mentions conversational naturalness, human‑likeness, warmth, etc.)
ethical	Binary indicator (1 = review mentions trust, privacy, bias, fairness, etc.)

processed_text	Lemmatised, stopword‑removed, lowercased text of the review (after cleaning)

How the Binary Indicators (Cognitive, Affective, Social, Ethical) Were Derived
Each binary indicator was created using dictionary‑based keyword matching on the processed_text field, with whole‑word matching (regular expression \bword\b). A review was coded as 1 for a given factor if any keyword from the corresponding dictionary appeared in its processed_text. The keyword dictionaries are:

Cognitive keywords
explain, explanation, step by step, step, detail, depth, accurate, accuracy, correct, reasoning, logic, understand, understanding, clarify, clear, comprehensive, thorough, insight, knowledge, learn, learning, teach, teaching, tutor, homework, assignment, problem solving, solution, answer, question, math, science, coding, programming, essay, writing, grammar

Affective keywords
encouraging, encouragement, motivate, motivating, motivation, support, supportive, patient, patience, kind, friendly, helpful, understanding, empathetic, empathy, care, caring, nice, pleasant, positive, encouraged, inspired, confidence, frustrat, annoying, angry, upset, disappointed

Social keywords
conversation, conversational, natural, human, humanlike, like a friend, companion, interact, interaction, dialogue, chat, talk, speak, respond, response time, engaging, personable, warm, cold, robotic, mechanical

Ethical keywords
trust, trustworthy, reliable, bias, biased, fair, unfair, privacy, private, data, secure, safety, transparent, transparency, honest, manipulate, manipulative, ethical, ethics, responsible, accountability, mislead, misinformation, fake

Reviews were filtered for education keywords, cleaned (URLs, non‑letters, lowercase), kept ≥5 words, detected as English (langdetect), then tokenised, stopword‑removed, lemmatised (NLTK), and trimmed to ≥3 characters. Final cleaned text is processed_text.</dc:description>
    <dc:subject>Psychology</dc:subject>
    <dc:subject>Education</dc:subject>
    <dc:subject>ChatGPT</dc:subject>
    <dc:subject>Chatbot</dc:subject>
    <dc:contributor>Li, Le</dc:contributor>
    <dc:contributor>Law, Kian Aun </dc:contributor>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/54tbst239t.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/54tbst239t.1</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
    <dc:rights>http://creativecommons.org/licenses/by/4.0</dc:rights>
    <dc:relation>https://data.mendeley.com/datasets/54tbst239t</dc:relation>
    <dc:date>2026-06-11T05:13:38Z</dc:date>
</oai_dc:dc></metadata>
        </record>
    </GetRecord>
</OAI-PMH>
