Patricia Lasserre

Mostafa Abbasi, Rahnuma Islam Nishat, Corey Bond et al.

Purpose- The significance of business processes has fostered a close collaboration between academia and industry. Moreover, the business landscape has witnessed continuous transformation, closely intertwined with technological advancements. Our main goal is to offer researchers and process analysts insights into the latest developments concerning Artificial Intelligence (AI) and Machine Learning (ML) to optimize their processes in an organization and identify research gaps and future directions in the field. Design/methodology/approach- In this study, we perform a systematic review of academic literature to investigate the integration of AI/ML in business process management (BPM). We categorize the literature according to the BPM life-cycle and employ bibliometric and objective-oriented methodology, to analyze related papers. Findings- In business process management and process map, AI/ML has made significant improvements using operational data on process metrics. These developments involve two distinct stages: (1) process enhancement, which emphasizes analyzing process information and adding descriptions to process models, and (2) process improvement, which focuses on redesigning processes based on insights derived from analysis. Research limitations/implications- While this review paper serves to provide an overview of different approaches for addressing process-related challenges, it does not delve deeply into the intricacies of fine-grained technical details of each method. This work focuses on recent papers conducted between 2010 and 2024. Originality/value- This paper adopts a pioneering approach by conducting an extensive examination of the integration of AI/ML techniques across the entire process management lifecycle. Additionally, it presents groundbreaking research and introduces AI/ML-enabled integrated tools, further enhancing the insights for future research.

Zhehao Sun, Yuanyuan Xu, Chi Zhen et al.

While traditional game design prioritizes friction-free accessibility, the Soulslike subgenre has achieved commercial dominance through punishing difficulty and frequent failure. This paper challenges the conventional hedonistic paradigm of gaming to investigate the psychological mechanisms behind the Paradox of Failure. By integrating Csikszentmihalyi's Flow Theory with Juul's ludological framework, we propose the concept of Resilient Flow. We define this as a cognitive state wherein absorption is maintained not despite frustration but through the meaningful framing of it. To validate this model without invasive laboratory constraints, we conducted a qualitative text analysis of 600 helpful user reviews from Elden Ring, Sekiro: Shadows Die Twice, and Dark Souls III via the Steam Community platform. Findings reveal that long-term players linguistically reframe death as pedagogy rather than punishment and utilize vocabulary associated with rhythmic synchronization and meditative focus. We conclude that when difficulty is designed with clarity and fairness, it fosters an Ethics of Attention and transforms digital struggle into a profound experience of mastery and mindfulness.

Yuanyuan Xu, Zhehao Sun, Chi Zhen et al.

As the global climate crisis intensifies, 3D video games have emerged as powerful, interactive simulations for Environmental Education (EE). However, empirical assessment of their pedagogical efficacy remains epistemologically challenged. Traditional evaluation metrics, such as pre-post surveys, often suffer from response bias and fail to capture the nuanced, emergent psychological shifts players experience during gameplay. This paper proposes a novel, non-intrusive approach: utilizing Semantic Network Analysis (SNA) to map the 'unsupervised' cognitive structures of players. We scraped and qualitatively filtered 1,825 rich-text user reviews from Steam for two distinct titles representing opposing ecological philosophies: Eco (anthropocentric systemic management) and WolfQuest (biocentric embodied survival). By constructing co-occurrence networks and calculating topological metrics, we visualized the divergence in how players conceptualize human-nature relationships. Results indicate a fundamental pedagogical split: Eco promotes 'Socio-Political Cognition,' where environmental challenges are framed as legislative and economic frictions; conversely, WolfQuest fosters 'Effective Empathy,' where players internalize the fragility of life through the vulnerability of the avatar. We argue that semantic topology offers a rigorous methodological tool for serious games assessment, revealing that effective environmental education requires a strategic tension between systemic logic and emotional resonance.

John Brandon Graham-Knight, Jamil Fayyad, Nourhan Bayasi et al.

Class imbalance and label noise are pervasive in large-scale datasets, yet much of machine learning research assumes well-labeled, balanced data, which rarely reflects real world conditions. Existing approaches typically address either label noise or class imbalance in isolation, leading to suboptimal results when both issues coexist. In this work, we propose Conformal-in-the-Loop (CitL), a novel training framework that addresses both challenges with a conformal prediction-based approach. CitL evaluates sample uncertainty to adjust weights and prune unreliable examples, enhancing model resilience and accuracy with minimal computational cost. Our extensive experiments include a detailed analysis showing how CitL effectively emphasizes impactful data in noisy, imbalanced datasets. Our results show that CitL consistently boosts model performance, achieving up to a 6.1% increase in classification accuracy and a 5.0 mIoU improvement in segmentation. Our code is publicly available: CitL.

Mostafa Abbasi, Maziyar Khadivi, Maryam Ahang et al.

The application of artificial intelligence and machine learning in business process management has advanced significantly, however, the full potential of these technologies remains largely unexplored, primarily due to challenges related to data quality and availability. We present a novel framework called Fine-Tuned Offline Reinforcement Learning Augmented Process Sequence Optimization (FORLAPS), which aims to identify optimal execution paths in business processes by leveraging reinforcement learning enhanced with a state-dependent reward shaping mechanism, thereby enabling context-sensitive prescriptions. Additionally, to compare FORLAPS with the existing models (Permutation Feature Importance and multi-task Long Short Term Memory model), we experimented to evaluate its effectiveness in terms of resource savings and process time reduction. The experimental results on real-life event logs validate that FORLAPS achieves 31% savings in resource time spent and a 23% reduction in process time span. To further enhance learning, we introduce an innovative process-aware data augmentation technique that selectively increases the average estimated Q-values in sampled batches, enabling automatic fine-tuning of the reinforcement learning model. Robustness was assessed through both prefix-level and trace-level evaluations, using the Damerau-Levenshtein distance as the primary metric. Finally, the model's adaptability across industries was further validated through diverse case studies, including healthcare treatment pathways, financial services workflows, permit applications from regulatory bodies, and operations management. In each domain, the proposed model demonstrated exceptional performance, outperforming existing state-of-the-art approaches in prescriptive decision-making, demonstrating its capability to prescribe optimal next steps and predict the best next activities within a process trace.