The Impact of Generative Conversational Artificial Intelligence on the Lesbian, Gay, Bisexual, Transgender, and Queer Community: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Despite recent significant strides toward acceptance, inclusion, and equality, members of the lesbian, gay, bisexual, transgender, and queer (LGBTQ) community still face alarming mental health disparities, being almost 3 times more likely to experience depression, anxiety, and suicidal thoughts than their heterosexual counterparts. These unique psychological challenges are due to discrimination, stigmatization, and identity-related struggles and can potentially benefit from generative conversational artificial intelligence (AI). As the latest advancement in AI, conversational agents and chatbots can imitate human conversation and support mental health, fostering diversity and inclusivity, combating stigma, and countering discrimination. In contrast, if not properly designed, they can perpetuate exclusion and inequities. OBJECTIVE: This study aims to examine the impact of generative conversational AI on the LGBTQ community. METHODS: This study was designed as a scoping review. Four electronic scholarly databases (Scopus, Embase, Web of Science, and MEDLINE via PubMed) and gray literature (Google Scholar) were consulted from inception without any language restrictions. Original studies focusing on the LGBTQ community or counselors working with this community exposed to chatbots and AI-enhanced internet-based platforms and exploring the feasibility, acceptance, or effectiveness of AI-enhanced tools were deemed eligible. The findings were reported in accordance with the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). RESULTS: Seven applications (HIVST-Chatbot, TelePrEP Navigator, Amanda Selfie, Crisis Contact Simulator, REALbot, Tough Talks, and Queer AI) were included and reviewed. The chatbots and internet-based assistants identified served various purposes: (1) to identify LGBTQ individuals at risk of suicide or contracting HIV or other sexually transmitted infections, (2) to provide resources to LGBTQ youth from underserved areas, (3) facilitate HIV status disclosure to sex partners, and (4) develop training role-play personas encompassing the diverse experiences and intersecting identities of LGBTQ youth to educate counselors. The use of generative conversational AI for the LGBTQ community is still in its early stages. Initial studies have found that deploying chatbots is feasible and well received, with high ratings for usability and user satisfaction. However, there is room for improvement in terms of the content provided and making conversations more engaging and interactive. Many of these studies used small sample sizes and short-term interventions measuring limited outcomes. CONCLUSIONS: Generative conversational AI holds promise, but further development and formal evaluation are needed, including studies with larger samples, longer interventions, and randomized trials to compare different content, delivery methods, and dissemination platforms. In addition, a focus on engagement with behavioral objectives is essential to advance this field. The findings have broad practical implications, highlighting that AI's impact spans various aspects of people's lives. Assessing AI's impact on diverse communities and adopting diversity-aware and intersectional approaches can help shape AI's positive impact on society as a whole.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.016 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".