The rapid evolution of industrial systems demands a careful reconciliation of sustainability goals, beyond organizational success and financial profitability. While Industry 4.0 (I4.0) has driven significant advances in automation and process efficiency, its impact on the Sustainable Development Goals (SDGs) remains limited and primarily focused on economic outcomes. Responding to the need for deeper integration between digital transformation and sustainability, this study aims to map the existing literature on Lean Six Sigma (LSS) in the contexts of I4.0 and Industry 5.0 (I5.0), but also, to clarify how sustainability dimensions can be embedded into LSS practices. To achieve this, a scoping review was conducted, including all scientific research published up to submission date and sourced from reliable databases (Scopus and others). Through this comprehensive review, a notable gap in how LSS addresses the various dimensions of SDGs was identified. To address this, the present work introduces a novel conceptual framework —Lean Six Sigma 5.0 (LSS 5.0)— which repositions traditional process improvement tools within a human-centric, resilient, and sustainability-oriented manufacturing paradigm. This innovative model advances Sustainable Manufacturing (SM) by embedding sustainability at the core of quality and continuous improvement strategies, enabling organizations to respond to the evolving expectations of I5.0. Beyond its theoretical contribution, this work offers practical insights for industries seeking to align LSS initiatives with the broader goals of sustainable development. Given its theoretical nature, future research should validate this model through empirical case studies and industrial applications, supporting innovation in SM. The findings directly address the review’s objectives by mapping existing knowledge and proposing actionable pathways for integrating sustainability within LSS practices in the I5.0 era.

The recent emergence of conversational Artificial Intelligence (AI) agents has profoundly transformed learning and teaching practices in higher education. These tools offer multiple advantages, ranging from cognitive assistance to enhanced student autonomy and efficiency. However, their actual impact on academic performance remains understudied, and the existing research often presents contradictory findings. To address this gap, the present study is the first to employ a Genetic Algorithm (GA) and Multi-Layer Perceptron Neural Networks (MLPNNs) to evaluate the influence of Generative AI Tools (GAITs) on students’ academic outcomes. A structured questionnaire was administered to 294 students from three Moroccan engineering schools in order to collect data on their use of these tools. An initial attempt to predict their grades using a statistical approach showed that familiarity with GAITs contributed positively to academic performance but had limited accuracy (39%), highlighting the need for more robust methods. Therefore, a hybrid model based on neural networks optimized with a GA was developed to better capture the complex relationships between the explanatory variables and academic performance. The results indicate that the GAIT-related variables considered in this study, taken in isolation, have a limited predictive capacity for students’ academic outcomes. This finding suggests that the available data does not capture the full complexity of the factors shaping academic success in contexts involving GAITs use.

The transition toward Industry 5.0 (I5.0) is reshaping industrial systems around sustainability, Human-Centricity (HC), and resilience, yet most Predictive Maintenance (PdM) approaches remain focused on technical objectives inherited from Industry 4.0. To characterize the maturity of I5.0-aligned PdM research, a scoping review of the Scopus database was conducted, screening 238 articles and retaining 123 based on substantive-engagement criteria, followed by a weighted scoring protocol assessing the depth of engagement with the three I5.0 pillars. The review reveals a pronounced skew toward shallow integration, with only a minority of studies operationalizing sustainability, HC, and resilience jointly. Building on these findings, this paper proposes an implementation-oriented Predictive Maintenance 5.0 (PdM 5.0) framework structured around three interconnected layers. The framework introduces a structured Large Language Model-mediated human-in-the-loop interaction, an adaptive learning mechanism, and an energy and CO₂ estimation pathway linking equipment degradation to sustainability indicators. A preliminary industrial case study using operational data from a recycled-paper mill demonstrates the practical execution of three interconnected pathways of the framework: equipment health assessment, energy and CO₂ impact estimation, and human-in-the-loop interaction supported by LLM-mediated explanations. Layer-specific evaluation metrics are further proposed to support broader operationalization. This work contributes a scoping-review-grounded architecture that advances PdM from a purely technical function toward a holistic, I5.0-aligned paradigm, while providing initial empirical evidence of its operational feasibility and a structured basis for broader industrial validation.