## 1 Introduction  
Recent advances in Artificial Intelligence (AI) have led to the integration of AI components into an increasing number of software engineering projects, giving rise to what are now referred to as AI-based systems [1, 2]. Although these projects are often executed by experienced development teams, they introduce several new challenges distinct from those encountered in traditional software systems [3]. A key emerging challenge is the accumulation of new forms of technical debt (TD) specific to AI development, which compounds the existing debt typically present in conventional software [4], ultimately affecting system sustainability, maintainability, and evolvability. Technical debt in AI systems encompasses long-term maintenance difficulties and hidden costs resulting from rapid development, insufficient abstractions, and evolving data dependencies [5]. It is increasingly recognized that addressing such debt requires robust modularization, continuous monitoring, and improved design practices, all of which complicate the long-term adaptation of AI-based systems. Several recent studies have sought to describe and classify the types and characteristics of technical debt in AI systems [6], proposing guidelines, best practices, and software engineering principles to mitigate these risks [7]. A particular area of interest within this landscape is the development and operation of AI/ML competition platforms, whether commercial, academic, research-oriented, or hybrid [6, 8, 9, 10, 11, 12]. An AI/ML competition platform is a structured software environment designed to host and evaluate artificial intelligence challenges, typically involving tasks in machine learning or reinforcement learning. While such platforms are often delivered online, they may also function as standalone frameworks. Common features include predefined datasets, submission mechanisms, scoring systems, and access to computational resources. These platforms' primary aim is to identify effective AI solutions through competitive evaluation, while simultaneously serving educational and research purposes [13, 14, 15]. They vary widely by scientific domain (e.g., medicine, robotics, environmental science), target community (e.g., researchers, students, professionals), platform infrastructure (e.g., open-source, closed-source, cloud-based), feature set (e.g., code sharing, custom metrics), and pricing model (free or paid) [6]. Incentives offered to participants include monetary prizes, career opportunities, and academic credit. Competitions typically span predefined periods, ranging from hours to several months [12, 16], and emphasize rapid prototyping and iterative solution development. Participants often prioritize experimentation and responsiveness over producing polished final products. Upon competition completion, submissions are evaluated against predefined criteria such as algorithmic efficiency and resource optimization [16]. Among academic institutions, AI/ML competitions have gained significant popularity due to their engaging, experiential learning approach [17]. They not only accelerate research but also enhance student participation and instructional quality [14, 16, 18]. Nonetheless, a recurring observation is that student participants, particularly in academic settings, often engage only once to meet course requirements, frequently without adhering to established software engineering practices for AI (SE4 AI) [4, 19, 20, 21]. This limited engagement and absence of structured development processes may lead to technical debt accumulation both in participant codebases and in the hosting competition platforms. Moreover, competition organizers currently lack structured methods forquantifying technical debt present within their platforms, further impeding their ability to maintain long-term system sustainability and credibility. Motivated by these challenges, the present study seeks to systematically explore and address technical debt in AI/ML competition platforms.