The millennial generation is facing challenges in their career path and they believe that tertiary education can help them to equip better to tackle against. However, some students find it difficult to rush back to classroom due to work commitment. Fortunately, flexible education developed these years allows students to capture knowledge anytime and anywhere easier. In order to deliver courses in line with students’ need, many universities have considered offering alternative studying modes, such as flexible method, to enrich the course delivery. Using a case study, this paper investigates the delivery approach adopted by a school of a well-known university in Australia. This School offers architecture and construction management courses and has successfully adopted the flexible approach, with the aid of various online teaching and learning tools: the Cloud, Elluminate Live!, EchoSystem, Mediawiki and ePortfolio, in delivering subjects. It is welcomed by various cohorts of students. Not only the student numbers have been increased, but the School is also the first preference when students opting architecture and construction management studies. Statistics also indicate students’ satisfaction and course experience are improved. The success of this School proves itself to be an exemplar for other educators planning for flexible delivery.

Artificial intelligence (AI) has revolutionized higher education. The rapid adoption of artificial intelligence in education (AIED) tools has significantly transformed educational management, specifically in self-directed learning (SDL). This study examines the factors influencing Indonesian higher education students' intention to adopt AIED tools for self-directed learning using a combination of the Theory of Planned Behavior (TPB) with additional theories. A total of 322 university students from diverse academic backgrounds participated in the structured survey. This study utilized machine learning it was Artificial Neural Networks (ANN) to analyze nine factors, including attitude (AT), subjective norms (SN), perceived behavioral control (PBC), optimism (OP), user innovativeness (UI), perceived usefulness (PUF), facilitating conditions (FC), perception towards ai (PTA), and intention (IT) with a total of 41 items in the questionnaire. The model demonstrated high predictive accuracy, with SN emerging as the most significant factor to IT, followed by AT, PBC, PUF, FC, OP, and PTA. User innovativeness was the least influential factor due to the lowest accuracy. This study provides actionable insights for educators, policymakers, and technology developers by highlighting the critical roles of social influence, supportive infrastructure, and student beliefs in shaping AIED adoption for self-directed learning (SDL). This research not only fills an important gap in the literature but also offers a roadmap for designing inclusive, student-centered AI learning environments that empower learners and support the future of SDL in digital education.