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In recent years, there has been a rise in recognition of the need for computing education to bridge the gap between academia and industry. In addition, educational researchers are also interested in increasing student engagement by grounding learning experiences in real-life concerns, community issues, or personal interests. Unfortunately, traditional lecture-based teaching techniques often fail to prepare students for the challenges they will face in real-world software development scenarios. Project-Based Learning (PjBL) takes a different approach by immersing students in real-world software engineering projects, allowing them to apply theoretical knowledge in practical contexts, building practical skills, fostering critical thinking, and improving problem-solving abilities. Prior literature reviews have explored aspects of PjBL in computing education, such as communication support, educational effectiveness, sprint organization, and capstone course design. However, no literature review extensively and comprehensively examines the following questions as a whole: where PjBL is used, how it is taught, why it should be used, and what challenges to expect in software-related computing courses. The review takes a systematic approach, incorporating a thorough search strategy across four academic databases and targeting keywords associated with PjBL and software computing in higher education. A total of 34 PjBL course attributes were extracted from 184 selected primary studies, which contributed to answering six research questions: (1) What computing courses use PjBL? (2) What is the nature of software projects used? (3) How are these projects organized? (4) How are students assessed and evaluated? (5) What are the reported impacts of PjBL? and (6) How are students supported throughout the projects? The literature review makes four key contributions: a description of the nature of software projects used and how these projects are organized, a highlight of the impacts of PjBL and the methods used to measure those impacts, a summary of the various forms of support provided to students throughout their projects, and the list of challenges encountered in implementing PjBL and recommendations to alleviate those challenges. This comprehensive review offers new insights and serves as a catalog of best practices for computing educators. 

Realizing the promise of artificial intelligence (AI) to accelerate scientific progress and deliver technological impact depends on how effectively AI can be integrated into real-world decision-making processes. As Peter Norvig states, “Somewhat remarkably, almost all AI research until very recently has assumed that the performance measure can be exactly and correctly specified in the form of a utility or reward function” [S. J. Russell and P. Norvig, Artificial Intelligence: A Modern Approach (Pearson, 2020)]. Once the reward function is known, any problem can be formulated as an optimization or search problem—the areas well explored within the AI community. However, while the long-term objectives of a specific activity are often well defined, constructing short-term rewards that remain aligned with those goals and consistent with real-world constraints remains a major unresolved challenge. Such alignment has been achieved in domains such as chess, Go, and supervised machine learning problems, where objectives are well defined and easily simulated. However, no universal solution exists for defining intermediate rewards for complex, evolving scientific goals, which remains an open challenge. Correspondingly, the key to operationalizing automated instruments, integrating multi-instrument self-driving laboratories, and building geographically distributed research facilities is to generate experiment-aligned, probabilistic, domain-specific reward functions. These rewards must be consistent with long-term experimental objectives while remaining actionable on the timescales of decision-making on laboratory tools in microscopy and materials synthesis labs—proper reward definition operationalizes scientific intent. Here, we review the extant reward structures in the physical sciences and summarize opportunities for reward design informed by physical principles, human heuristics, and LLM-based reasoning.