Introduction:
Academic dishonesty is a growing concern in higher education, especially with the rise of generative artificial intelligence. This research explores the frequency and reasons for academic dishonesty. Enhancing awareness can create a positive environment for students.

Research Questions:
a) Is there a significant difference in the prevalence of academic dishonesty among students at higher education institutions?
b) How do ethnicity, marital status, and employment status influence the frequency of academic dishonesty?
c) What is the relationship between gender, ethnicity, and employment status and cheating on exams, plagiarism, outside help, prior cheating, falsification, and lying about academic assignments?

Research Methods:
This study adopts a mixed methods research design, incorporating both quantitative and qualitative phases. In the quantitative phase, a questionnaire was utilized; in the qualitative phase, a content analysis was used.

Findings:
- Quantitative Findings:
To answer research question one, “Is there a significant difference in the prevalence of academic dishonesty among students at higher education institutions?” A linear regression tested the impact of academic dishonesty awareness on its frequency, revealing that awareness explained 28% of the variation, and the difference was significant [F(1,108) = 3.14, p = .045].
To answer question two, “How do ethnicity, marital status, and employment status influence the frequency of academic dishonesty?” a partial correlation analysis was conducted. The results indicated a significant relationship between employment status and the frequency of academic dishonesty [r(107) = .261, p = .006].
For question three, "What is the relationship between gender, ethnicity, and employment status and cheating on exams, plagiarism, outside help, prior cheating, falsification, and lying about academic assignments?" the authors received the findings that there was gender in cheating exam.
- Qualitative Findings:
The integration of Generative AI (GAI) into academic research and practices presents several ethical challenges. There are three thematic concepts: ‘ChaptGPT,’ ‘Integrity,’ ‘Academic,’ ‘Tools,’ ‘Instructor,’ and ‘faculty.’
The release of ChatGPT by OpenAI has sparked significant debate about its impact on education, particularly regarding academic integrity. ChatGPT can generate human-like text, raising concerns that students may use it to complete assignments, thereby undermining the value of academic writing. Despite these fears, ChatGPT also presents legitimate challenges to academic integrity, such as evading plagiarism detection and facilitating contract cheating.

Discussion:
The quantitative findings revealed significant correlations between academic dishonesty and awareness, ethnicity, and employment status. Linear regression and partial correlation analyses demonstrated associations between awareness and frequency of dishonesty. Gender showed significant relationships with various forms of academic dishonesty. However, relationships between ethnicity and some forms of dishonesty were not significant. Qualitative findings underscored ethical challenges in GAI integration, including authorship, data privacy, bias and fairness, accountability, transparency, and impacts on educational practices. These results emphasize the multifaceted nature of ethical considerations in GAI application within academia.

Keywords: Academic integrity, mixed methods research, academic dishonesty.