Plagiarism Policies in Higher Learning Institutions and Their Effectiveness in Mitigating Artificial Intelligence Breach of Originality in Arusha Region, Tanzania

Betrod Msigwa
Olmotonyi Forest Secondary School, Arusha.
Email: betrod00031@gmail.com

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.