Conectar
Large language model tools are increasingly used in higher education, offering opportunities to support self-directed learning. In nursing education, course-specific AI virtual tutors may provide contextualised support while addressing concerns about content accuracy and alignment; yet empirical evidence remains limited.
This study evaluated the use and perceived impact of a co-designed AI-powered virtual tutor embedded in a graduate-level Master of Nursing (MN) course. We explored how students used the tutor, their perceptions of benefits and limitations, and its influence on learning and engagement.
A pilot study using a mixed-methods explanatory sequential design was employed. The tutor was trained on course-specific materials and integrated into the institutional learning management system. Data included anonymised usage logs and user interactions coded using Bloom's Taxonomy of Educational Objectives, post-course surveys assessing AI self-efficacy, usability, and learning impact, and semi-structured interviews with students and teaching assistants (TAs). Quantitative and qualitative strands were integrated through a joint display.
A total of 651 interactions by individuals within a group of ~120 MN students were logged. Interactions peaked in evenings and around assignment deadlines. Most interactions reflected lower-order education processes, with more application and analysis later in the course. Eleven participants completed surveys; students reported high AI self-efficacy and moderate tutor use. Perceived usefulness was mixed, but most reported the tutor enhanced both lower- and higher-level learning and recommended its future use. Interviews revealed that students valued the tutor's immediacy and course-specific accuracy, while TAs noted efficiency gains. Reported challenges included usability issues, scope limitations, privacy concerns, and risk of over-reliance on the tool.
A co-designed AI virtual tutor was feasible and valued for contextual relevance, though perceived usefulness was variable. Findings support responsible, pedagogically integrated use of AI tutors in graduate nursing education.
To explore how community-dwelling older adults with multimorbidity experience, enact and navigate daily self-care using the Caring Life-Course Theory to identify opportunities for strengthening self-care and self-management support.
Qualitative descriptive study.
Semi-structured interviews were conducted with community-dwelling older adults aged ≥ 50 years living with two or more chronic conditions across three Australian states and territories. Data were analysed inductively and deductively using qualitative content analysis. Inductive coding was followed by theory-informed analysis to interpret self-care capability, capacity, care networks and system supports.
Eighteen participants (mean age = 70.9 years) described self-care as an adaptive, experience-based process influenced by lived experience, health transitions, informal care networks and system responsiveness. Participants generally demonstrated agency and resourcefulness in managing complex and changing care needs, often learning through trial and error. Psychosocial and relational needs were frequently under-recognised in healthcare encounters, requiring individuals and informal carers to compensate for fragmented, inconsistent support. Self-care capability and capacity were shaped by experiential learning, health and self-care literacy and access to informal and online resources, particularly where formal education was limited or unavailable.
Self-care for older adults with multimorbidity is shaped by dynamic interactions between personal capability, relational support and system-level factors across the life-course. The Caring Life-Course Theory provides a comprehensive approach for understanding these interdependencies and identifying opportunities for intervention.
Findings highlight the need to strengthen coordinated, person-centred and relationally grounded approaches to self-care and self-management in primary and community care, including improved access to evidence-informed resources and anticipatory support.
This study is reported in accordance with the Consolidated Criteria for Reporting Qualitative Studies.
Participants contributed through semi-structured interviews and provided feedback on study findings.
What problem did the study address? Community-dwelling older adults living with multimorbidity are expected to engage in self-care, yet little is known about how they experience, enact and sustain daily self-care, particularly when psychosocial and relational needs are inconsistently recognised within health and social care systems. What were the main findings? Self-care was characterised as an adaptive, experiential process shaped by life experience, informal support networks and system responsiveness. Participants frequently compensated for under-recognised psychosocial and relational needs through informal and online supports and resources. Where and on whom will the research have an impact? Findings can inform clinicians, service providers and policymakers in primary, community and aged care settings by identifying opportunities to strengthen coordinated, person-centred and relational self-care and self-management support for older people with multimorbidity.
FreshRSS 