The Future of Education

For future higher education, it is paramount to ensure that computer science students successfully complete their bachelor’s theses on time which is a growing challenge due to increased complexity in both scientific approaches, engineering skills and the AI tools palette entering the stage. Problem solving skills are fundamental for students to be able to navigate through these information flows to be able to express and formulate their own set up criteria following a logical order to be able to reflect and state outcomes from this process. The process includes a reverse engineering assessment approach at the same time as the formal requirements demand a sequential writing order including both natural and programming languages described and motivated. We require parallel processes where we need to support students in how they base a problem, how to gain insights from previous industrial and academic attempts by others, formulate research questions from a scientific perspective and at the same time as hardware and/or software implementation is based on this approach. Many students struggle with understanding scientific prerequisites, their role in relation to this often due to a lack of comprehension of what this means when to formulate, prepare and motivate themselves in an academic context. It is paramount to highlight joint necessities for scientific and engineering problem formulations to reach higher quality and timely aspects [1]. Incorporating AI-driven tools [2] can be a way to increase and support curiosity around searching for information and answers to problems or concepts being unfamiliar. Iterative feedback loops [3] can significantly improve students’ ability to formulate research questions, conduct systematic investigations, and manage their time effectively. Important though, it is not to reduce one’s own problem-solving capacity by only copying paste actions solely without understanding its meaning(s). The findings suggest that early engagement through research methodology course not only improves the quality of bachelor theses but also enhances students’ confidence and readiness for future professional and academic challenges. The quality for the students is to explain the unexplainable and/or its complexity even when using AI tools for reaching enhancements. Student throughput and the evaluations from course feedback can indicate how to improve.