The purpose of this study is to reveal the pull and push factors that influence the decisions of administrators and practitioners about online courses offered to students on campus in terms of teaching-learning, course content procurement, and assessment-evaluation dimensions. The study was carried out using nested multi-case studies as a qualitative research method. The sample of the study consisted of four Distance Education Centers (DEC) with at least five years of experience, selected by the snowball and maximum variation sampling methods. The data were obtained using individual semi-structured interviews, institution documents, and Learning Management Systems (LMS), with 24 people working as administrators, content development specialists, program coordinators, and IT specialists at these institutions. It was found that factors like the interaction between students and teachers, legislation, budget, human resources, and organized structuring helped the DECs decide which models to use in the teaching-learning, course content procurement, and assessment-evaluation dimensions.

Artificial intelligence-supported online assessment and evaluation (AI-SOAE) systems have the potential to transform assessment and evaluation processes in higher education; however, there is a need for a comprehensive evaluation of the pedagogical, technical, and ethical dimensions of these systems. In this regard, the study systematically examined the characteristics of AI-SOAE systems within the framework of a SWOT analysis. A systematic literature review method was used in the research. Ninety-eight studies published between 2018 and 2025 in the Web of Science (WoS), Scopus, and Education Resources Information Centre (ERIC) databases were analysed in accordance with PRISMA guidelines. The findings show that the strengths of AI-SOAE systems include personalised feedback, advanced assessment processes, increased efficiency, and data-driven decision support mechanisms. In contrast, technical infrastructure limitations, algorithmic biases, data management issues, and negative user experiences were identified as the weaknesses of the systems. The analysis also reveals that AI-SOAE systems offer significant opportunities in terms of pedagogical transformation, digitalisation, and institutional competitiveness; however, they face threats such as data security, ethical concerns, risks to academic integrity, and limited empirical evidence. Consequently, AI-SOAE systems offer context- and application-dependent potential for supporting assessment and evaluation practices in higher education. Effectively realising this potential depends on strengthening technical infrastructure, managing algorithmic risks, and developing transparent, ethical, and institutional policy frameworks. By synthesising the literature in the field of AI-SOAE in a structured manner, this study provides a comprehensive assessment for researchers, educators, and policymakers.

The aim of the study is to examine AI tools that will increase student engagement in online courses and provide support to teachers. In this context, AI tools were examined as a result of the literature review. These tools were examined under six headings: adaptive learning platforms, customized material creation tools, feedback and guidance tools, exam and test creation tools, student engagement monitoring tools, and classroom management tools. Some of the tools analyzed are ALEKS, SlidesAI, Squirrel, MagicForm, Quizbot, Nearpod, and Knewton Alta. It can be argued that these tools provide a range of benefits such as improving learning outcomes by increasing student engagement in online courses, personalizing learning experiences, improving teaching quality, providing tailored courses for students, and improving assessment. However, concerns such as unethical use of student data, limitations in measuring and assessing the learning process, and the inability of automated assessment systems to adequately assess emotion and creativity should be taken into account when using these tools.

Equality of opportunity in education requires access to quality learning opportunities for all, but socioeconomic inequalities, geographical barriers, disability and the digital divide limit this access. Artificial intelligence (AI) technologies can reduce these inequalities through personalized learning, disability-accessible content and multilingual-cultural adaptations. They can also ease teachers' workload and facilitate early identification of at-risk students. However, the benefits of AI may not trickle down equally to all groups; lack of access to technology and biases in algorithms may exclude some groups. AI tools therefore need to be transparent, explainable and ethically grounded. This chapter examines the dimensions of the digital divide, the contributions of AI, the effects of algorithmic biases, and teachers' AI literacy, and offers recommendations for inclusive AI design and policy.

The spread of artificial intelligence in the labor market is reshaping students’ employment expectations and creating concerns about the job search process. This study examines how students’ artificial intelligence related anxiety and attitudes influence their post graduation job finding anxiety. Although individual attitudes and concerns related to artificial intelligence have been increasingly explored in recent years, limited research has directly addressed the relationship between these variables and labor market-related concerns. There remains a need for multivariate studies with large samples that comprehensively investigate the interplay between technological transformation and employment uncertainty. This quantitative research employed a correlational survey design and included 1,057 students from various faculties and departments across 35 universities in Türkiye. Data were collected through online surveys using convenience and snowball sampling methods. The analysis involved statistical techniques such as correlation, independent samples t-test, ANOVA, and Cohen’s d. The findings showed that higher anxiety and negative attitudes toward artificial intelligence are positively associated with job finding anxiety. In addition, female students, social science majors, and second year students reported higher anxiety levels. The results were interpreted within the frameworks of the Technology Acceptance Model and Social Cognitive Career Theory, suggesting that students’ perceptions of technological transformation influence their career planning and sectoral expectations. In this context, the study underlines the need for a review of higher education policies to better support students' career paths in the age of artificial intelligence.