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@article{207493,
        author = {Dr.Kavita Jain and Vibha Singh Gautam},
        title = {When AI Well-being Widens the Gender Gap: A Systematic Review and the GRAW Framework for Organisational Sustainability},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {1671-1681},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207493},
        abstract = {Purpose: Despite rapid growth in AI-enabled workplace well-being interventions, organisations deploy these tools without adequately accounting for the gender-differentiated experience of work. Working women carry a structural double burden paid employment plus unpaid domestic labour that AI well-being systems routinely ignore. This systematic literature review (SLR) examines evidence on AI well-being interventions through a gender-sensitive lens, assesses implications for organisational sustainability, and proposes the Gender-Responsive AI Well-being (GRAW) Framework.
Design/methodology/approach: Following PRISMA 2020 guidelines (Page et al., 2021), 68 peer-reviewed articles published between 2015 and 2025 were identified, screened, and synthesised across technology, healthcare, finance, higher education, and public administration sectors. Quality was assessed using the JBI Critical Appraisal Checklist (Joanna Briggs Institute, 2020). Thematic synthesis (Thomas and Harden, 2008) identified four convergent interpretive themes.
Findings: AI well-being tools deliver measurable benefits distributed unequally across gender lines. Women with caregiving responsibilities derive significantly smaller gains, experience the double burden as a structural adoption barrier, face algorithmic bias and privacy-trust deficits, and without gender-responsive design generate compounding sustainability failures including elevated voluntary turnover and diversity erosion.
Research limitations/implications: The review covers English-language peer-reviewed literature; evidence from the Global South, including India, remains limited. The GRAW Framework requires empirical validation. Future longitudinal and experimental studies should test specific design principles.
Practical implications: Organisations should gender-audit existing AI well-being tools before extending deployment. HR technology vendors can differentiate through GRAW-aligned design. Professional bodies should mandate double-burden-aware design in member well-being tools.
Social implications: Gender-responsive AI well-being design addresses structural inequality in workplaces, with particular relevance to India and other contexts where domestic role expectations remain highly gendered. ESG and BRSR integration positions this as a governance rather than a merely operational concern.
Originality/value: This is among the first SLRs synthesising AI well-being interventions, the gendered double burden, and organisational sustainability. The GRAW Framework, and two new constructs Domestic Demand Overflow (DDO) extending JD-R theory, and Temporally Conditional Perceived Usefulness (TCPU) extending TAM provide an original theoretical and practical architecture for gender-equitable AI well-being.},
        keywords = {Gender; double burden; AI well-being; digital workplace; working women; organisational sustainability; burnout; systematic review; GRAW framework; flexible working; algorithmic bias; India},
        month = {August},
        }