Research has indicated that adult romantic attachment is influential and important to sexual and relationship satisfaction. Sexual desire, although not a direct focus of attachment literature, is highly related to sexual and relationship satisfaction, suggesting it may also be impacted by attachment style in romantic couples. However, the research conducted on sexual desire, sexual satisfaction, and attachment has been largely heterocentric, making it difficult to determine whether the findings documented in the literature thus far are relevant in a sexually diverse group of individuals. The current study aimed to better understand the way that attachment style may impact sexual desire, sexual satisfaction, and relationship satisfaction in a sample of sexually diverse men and women. In total, 955 individuals (63.4% cisgender women, 30.7% cisgender men, 6.0% genderqueer, 54.8% straight, 20.4% bisexual, 18.4% gay) participated in a web-based study examining relationship dynamics. Three multivariate regression models indicated that attachment style significantly predicted relationship satisfaction (29% of variance accounted for), sexual satisfaction (19% of variance accounted for), and sexual desire (4% of variance accounted for). Attachment style appears to be a more important contributing factor to satisfaction than desire among diverse sexual orientations. Clinical implications and future directions are discussed.

Purpose of Review: Millions of people now use generative artificial intelligence (GenAI) tools in their daily lives for a variety of purposes, including sexual ones. This narrative literature review provides the first scoping overview of current research on generative AI use in the context of sexual health and behaviors. Recent Findings: The review includes 88 peer-reviewed English language publications from 2020 to 2024 that report on 106 studies and address four main areas of AI use in sexual health and behaviors among the general population: (1) People use AI tools such as ChatGPT to obtain sexual information and education. We identified k = 14 publications that evaluated the quality of AI-generated sexual health information. They found high accuracy and completeness. (2) People use AI tools such as ChatGPT and dedicated counseling/therapy chatbots to solve their sexual and relationship problems. We identified k = 16 publications providing empirical results on therapists’ and clients’ perspectives and AI tools’ therapeutic capabilities with mixed but overall promising results. (3) People use AI tools such as companion and adult chatbots (e.g., Replika) to experience sexual and romantic intimacy. We identified k = 22 publications in this area that confirm sexual and romantic gratifications of AI conversational agents, but also point to risks such as emotional dependence. (4) People use image- and video-generating AI tools to produce pornography with different sexual and non-sexual motivations. We found k = 36 studies on AI pornography that primarily address the production, uses, and consequences of – as well as the countermeasures against – non-consensual deepfake pornography. This sort of content predominantly victimizes women and girls whose faces are swapped into pornographic material and circulated without their consent. Research on ethical AI pornography is largely missing. Summary: Generative AI tools present new risks and opportunities for human sexuality and sexual health. More research is needed to better understand the intersection of GenAI and sexuality in order to a) help people navigate their sexual GenAI experiences, b) guide sex educators, counselors, and therapists on how to address and incorporate AI tools into their professional work, c) advise AI developers on how to design tools that avoid harm, d) enlighten policymakers on how to regulate AI for the sake of sexual health, and e) inform journalists and knowledge workers on how to report about AI and sexuality in an evidence-based manner.

Research has demonstrated a strong link between relationship and sexual satisfaction in the context of romantic relationships. However, little research has examined this in the context of a dyadic longitudinal sample, where both partner and actor effects are considered over time. The current study aimed to assess directional fit of the link between relationship and sexual satisfaction with the actor–partner interdependence model (APIM) on the cross-sectional and longitudinal level. Data were collected online from 204 mixed-sex couples (408 individuals) in an online quantitative longitudinal study (baseline, 2 months, and 4 months). The average age of participants was 34 and participants were in a relationship for an average of nine years. Results from the cross-sectional APIM indicated that relationship satisfaction as a predictor of sexual satisfaction provided a better fit to the data. The over-time APIM further supported this directionality. Although prior research has shown mixed results, with some studies providing support for a bidirectional model, the current results indicated that relationship satisfaction precedes sexual satisfaction. This provides additional support that targeting relationship building to improve relationship satisfaction may be a fruitful approach to improving sexual satisfaction in relationships. Implications of the findings and directions for future research are discussed.

Infidelity can be a disruptive event in a romantic relationship with a devastating impact on both partners’ well-being. Thus, there are benefits to identifying factors that can explain or predict infidelity, but prior research has not utilized methods that would provide the relative importance of each predictor. We used a machine learning algorithm, random forest (a type of interpretable highly non-linear decision tree), to predict in-person and online infidelity across two studies (one individual and one dyadic, N = 1,295). We also used a game theoretic explanation technique, Shapley values, which allowed us to estimate the effect size of each predictor variable on infidelity. The present study showed that infidelity was somewhat predictable overall and interpersonal factors such as relationship satisfaction, love, desire, and relationship length were the most predictive of online and in person infidelity. The results suggest that addressing relationship difficulties early in the relationship may help prevent infidelity.