Can high quality listening predict lower speakers' prejudiced attitudes?
Abstract
Theorizing from humanistic and motivational literature suggests attitude change may occur because high-quality listening facilitates the insight needed to explore and integrate potentially threatening information relevant to the self. By extension, self-insight may enable attitude change as a result of conversations about prejudice. We tested whether high-quality listening would predict attitudes related to speakers' prejudices and whether self-insight would mediate this effect. Study 1 (preregistered) examined scripted conversations characterized by high, regular, and poor listening quality. In Study 2, we manipulated high versus regular listening quality in the laboratory as speakers talked about their prejudiced attitudes. Finally, Study 3 (preregistered) used a more robust measure of prejudiced attitudes to testing whether perceived social acceptance could be an alternative explanation to Study 2 findings. Across these studies, the exploratory (pilot study and Study 2) and confirmatory (Studies 1 & 3) findings were in line with expectations that high, versus regular and poor, quality listening facilitated lower prejudiced attitudes because it increased self-insight. A meta-analysis of the studies (N = 952) showed that the average effect sizes for high-quality listening (vs. comparison conditions) on self-insight, openness to change and prejudiced attitudes were, ds = 1.19, 0.46, 0.32 95%CIs [0.73, 1.51], [0.29, 0.63] [0.12, 0.53], respectively. These results suggest that when having conversations about prejudice, high-quality listening modestly shapes prejudice following conversations about it, and underscores the importance of self-insight and openness to change in this process.
Social-based learning and leadership in school: conflict management training for holistic, relational conflict resolution
Eli Vinokur, Avinoam Yomtovian, Marva Shalev Marom, Guy Itzchakov and Liat Baron
Listening
Navigating conflicts is crucial for promoting positive relationships between
pupils, teachers, and parents. The objective of this paper is to present Social-
Based Learning and Leadership (SBL), an innovative approach to group dynamics
and conflict resolution within the school setting, aiming to foster meaningful
relationships and personal and social growth. The methods of SBL focus on
group evolution by navigating conflicts rooted in higher needs while balancing
the interplay of separation and connection. It proactively embeds prosocial
values and conduct into the school culture, with teachers prioritizing the
wellbeing of others, fostering shared problem-solving, and positive feedback
amid conflicts. Teachers acquire tools to transform the classroom into a “social
laboratory” and constructmeaningful partnerships with parents. Practical conflict
management within the SBL framework involves dynamic group discussions,
shifting fromother blaming to accountability, and reflective group introspection.
Experiential learning through crafted case studies and role-plays enhances
students’ conflict management skills by fostering perspective-taking and
inclusiveness.We conducted a qualitative case study in an SBL training in a school
from 2020 to 2023. These conflict management processes allow the school
community to reimagine conflict as an invaluable educational opportunity,
equipping pupils with essential soft skills for navigating the challenges of the
21st century.
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Exploring the connecting potential of AI: Integrating human interpersonal listening and parasocial support into human-computer interactions
Netta Weinstein, Guy Itzchakov, Michael R. Maniaci
Attitudes
Conversational artificial intelligence (AI) can be harnessed to provide supportive parasocial interactions that rival or even exceed social support from human interactions. High-quality listening in human conversations fosters social connection that heals interpersonal wounds and lessens loneliness. While AI can furnish advice, listening involves the speakers’ perceptions of positive intention, a quality that AI can only simulate. Can such deep-seated support be provided by AI? This research examined two previously siloed areas of knowledge: the healing capabilities of human interpersonal listening, and the potential for AI to produce parasocial experiences of connection. Three experiments (N = 668) addressed this question through manipulating conversational AI listening to test effects on perceived listening, psychological needs, and state loneliness. We show that when prompted, AI could provide high-quality listening, characterized by careful attention and a positive environment for self-expression. More so, AI’s high-quality listening was perceived as better than participants’ average human interaction (Studies 1–3). Receiving high-quality listening predicted greater relatedness (Study 3) and autonomy (Studies 2 and 3) need satisfaction after participants discussed rejection (Study 2–3), loneliness (Study 3), and isolating attitudes (Study 3). Despite this, we did not observe downstream lessening of loneliness typically observed in human interactions, even for those who were high in trait loneliness (Study 3). These findings clearly contrast with research on human interactions and hint at the potential power, but also the limits, of AI in replicating supportive human interactions.
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