Kennedy Omondi Otieno
St. Augustine University of Tanzania (SAUT), Arusha.
Email: omondiken2016@gmail.com

Charles Gervas Rufyiriza
St. Augustine University of Tanzania (SAUT), Arusha.
Email: rufcharles@gmail.com

Abstract: This study assessed the plagiarism policies in public and private higher learning institutions (HLIs) and their effectiveness towards mitigating Artificial Intelligence (AI) assisted breach of originality. The study tested if there is statistically significant difference between the effectiveness of the plagiarism detection software in public and private HLIs on mitigating AI assisted breach of originality and significant difference between the number of times students’ work is subjected to testing for plagiarism in public and private HLIs. The study was grounded on Deterrence Theory. The study employed convergent mixed methods and correlational research design and tested hypotheses using T-test and Chi-square. Target population included 200 course instructors, 1200 students and 7 IT experts from 7 public and private HLIs, with a final sample of 147 respondents. Criterion purposive sampling was used to select seven system administrators while 20 course instructors and 120 students were selected using stratified and simple random sampling. Questionnaire and structured interview were used to collect data. Cronbach coefficient Alpha was employed to test for reliability of instruments; QS=0.759 & QCI=0.738. Credibility and dependability of qualitative data was established through detailed reporting of the research process. Descriptive statistics and thematic analysis were employed. All the two hypotheses yielded the results of p> 0.05 hence the null hypotheses were accepted. In conclusion, both public and private HLIs have established plagiarism policies aimed at addressing AI-assisted breach of originality although they’re not effective and recommended that institutions should more advance tools and guidelines in detecting AI plagiarism in academic work.

Abstract: The study assessed how psychosocial support influences the emotional and economic well-being of caregivers of cancer patients at KUTRRH in Nairobi, Kenya. Grounded in the Biopsychosocial Model and Family Systems Theory, it explored four key areas: emotional support, financial assistance, social support networks, and informational support. A cross-sectional design was used, involving 200 caregivers and 35 healthcare workers. Data collection methods included questionnaires, interviews, and focus group discussions. Findings revealed that most caregivers received moderate levels of emotional and financial support. Emotional support, particularly from family and friends, was rated highly (mean = 4.0), while counseling services and support groups were moderately available. Financial support was perceived as accessible and helpful (mean = 3.9). Statistical analysis showed a significant positive correlation among all four psychosocial support variables and family quality of life (r = 0.51 to 0.64, p < .01). Regression analysis indicated that psychosocial support accounted for 5.7% of the variability in family quality of life (R² = .057, p = .015). The study concludes that psychosocial support, especially emotional and financial assistance, plays a crucial role in reducing caregiver stress and improving family well-being. It recommends strengthening mental health services, increasing awareness, improving financial support systems, and establishing structured peer support networks for caregivers.