Integrating Social Exchange and Objectification Theories in AI-Driven Platforms for Enhancing Gender-Based Violence Reporting and Virtual Psychosocial Support in Zimbabwean Higher and Tertiary Education Institutions

July Ndemo, Monica Madyembwa and Jemitias Mapira
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July Ndemo: Department of Physics, Geography and Environmental Science, School of Natural Science, Great Zimbabwe University, Masvingo, Zimbabwe.
Monica Madyembwa: Department of Mathematics and Computer Science, School of Natural Science, Great Zimbabwe University, Masvingo, Zimbabwe.
Jemitias Mapira: Department of Physics, Geography and Environmental Science, School of Natural Science, Great Zimbabwe University, Masvingo, Zimbabwe.

International Journal of Research and Innovation in Social Science, 2025, vol. 9, issue 3s, 6242-6255

Abstract: Gender-Based Violence in Zimbabwean Higher and Tertiary Education Institutions remains a significant issue, with many survivors reluctant to report incidents due to stigma, fear of retaliation, and institutional inaction. While various interventions exist, there remains a gap in understanding how theoretical frameworks can inform the development of artificial intelligence-powered platforms for secure reporting and virtual psychosocial support. This study addresses this gap by integrating Social Exchange Theory and Objectification Theory to guide the creation of an artificial intelligence-driven platform for Gender-Based Violence disclosure. Social Exchange Theory posits that survivors of gender-based violence evaluate the potential advantages of reporting, against the possible drawbacks. In contrast, Objectification Theory explores how the societal tendency to reduce women to mere physical objects, contributes to the internalization of shame, which in turn reduces the likelihood that survivors will to seek help. A stratified sampling methodology was utilized to ensure a comprehensive and representative sample. The sample was divided into strata based on key demographic variables such as age, gender, academic discipline, and socio-economic background. Data collection involved focus group discussions and in-depth interviews. This study explored the integration of Social Exchange Theory and Objectification Theory in the development of an artificial intelligence-powered model for Gender-Based Violence (GBV) reporting and virtual psychosocial support within Zimbabwean higher and tertiary education institutions. Preliminary findings indicated that 30% of participants believed that incorporating these theories into the AI model would enhance survivors’ willingness to report GBV, addressing concerns related to cost-benefit analysis and societal objectification. Additionally, 35% emphasized the necessity of cognitive-behavioral therapy (CBT)-based virtual support to improve mental health outcomes, while 25% highlighted the importance of embedding technological diffusion frameworks to ensure effective adoption and sustainability. The study recommends a multidisciplinary approach to AI model development, incorporating sociological, psychological, and technological perspectives to create a scalable and ethical solution for GBV prevention and support.

Date: 2025
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