Enhancing or Hindering? The Influence of Generative AI on Critical Thinking and Collaborative Learning

The rise of generative AI tools has introduced new dynamics into university classrooms, particularly in how students collaborate and engage in critical thinking. This study investigates the effects of AI-assisted learning on student collaboration and cognitive engagement within a first-year undergraduate management course. Using an experimental design, we compared traditional group case study activities to AI-supported analysis. Results indicate that while AI facilitated faster information retrieval, it corresponded with reduced peer discussion and lower average performance compared to the control group. Post-experiment surveys and classroom observations suggest that AI use shifted student behaviors toward information consumption rather than collaborative knowledge construction. These findings highlight the complex influence of generative AI on foundational learning processes and point to the need for continued research on its long-term impacts in higher education contexts

Phonemes Classification Using the Spectrum

In this work, we present an automatic speech classification system for the Tamazight phonemes. We based on the spectrum presentation of the speech signal to model these phonemes. We have used an oral database of Tamazight phonemes. To test the system’s performances, we calculate the classification rate. The obtained results are satisfactory in comparison with the reference database and the quality of speech files

A Markov Decision Model for Area Coverage in Autonomous Demining Robot

A review of literature shows that there is a variety of works studying coverage path planning in several autonomous robotic applications. In this work, we propose a new approach using Markov Decision Process to plan an optimum path to reach the general goal of exploring an unknown environment containing buried mines. This approach, called Goals to Goals Area Coverage on-line Algorithm, is based on a decomposition of the state space into smaller regions whose states are considered as goals with the same reward value, the reward value is decremented from one region to another according to the desired search mode. The numerical simulations show that our approach is promising for minimizing the necessary cost-energy to cover the entire area

Green IS Research: A Modernity Perspective

Over the past two decades, the information systems community has become engaged in improving the environmental effects of information systems and technologies, which has given rise to the green IS field. Despite increasing interest, some have suggested that progress toward meaningful solutions for sustainability has been too slow. Responding to these concerns, we examine the development of green IS research using the modernity perspective to understand green IS’s evolution and to present alternative perspectives to motivate future research. From a sample of over 80 green IS papers published over a 15-year period, we identify four main patterns of modernity that are manifest in green IS research. These patterns include the importance of the individual in solving environmental problems; science as the main source of solutions; and the emergence of an artificial science approach, reliance on technology, and growth as businesses’ ultimate goals. Further, our analysis reveals that green IS research has started to demonstrate elements of a hyper-modernity perspective that emphasizes reflexivity. We argue that future green IS research should continue on this path and propose a conceptual framework inspired by hyper-modernity and centered on reflexivity that could serve as a guide for future research

Markovian Segmentation of Brain Tumor MRI Images

Image segmentation is a fundamental operation in image processing, which consists to di-vide an image in the homogeneous region for helping a human to analyse image, to diagnose a disease and take the decision. In this work, we present a comparative study between two iterative estimator algorithms such as EM (Expectation-Maximization) and ICE (Iterative Conditional Estimation) according to the complexity, the PSNR index, the SSIM index, the error rate and the convergence. These algorithms are used to segment brain tumor Magnetic Resonance Imaging (MRI) images, under Hidden Markov Chain with Indepedant Noise (HMC-IN). We apply a final Bayesian decision criteria MPM (Marginal Posteriori Mode) to estimate a final configuration of the resulted image X. The experimental results show that ICE and EM give the same results in term of the quality PSNR index, SSIM index and error rate, but ICE converges to a solution faster than EM. Then, ICE is more complex than EM

Exploratory study of Responsible Innovation: Toward a Holistic Approach to Sustainability

The Information Systems (IS) community has been called to address the important challenge of sustainable development, but progress continues to be slow. Elsewhere, responsible innovation (RI) has emerged as a framework to support the integration of sustainability considerations into the innovation process. The aim of this paper is to explore how organizations operationalize the main RI principles – anticipation, inclusion, responsiveness, and reflexivity. Based on a qualitative exploratory study, this paper develops insights into practices taken by organizations to address sustainability issues through their IS innovation processes. Our findings suggest that organizations operationalize RI principles highly or partially under the influence of five factors. A new understanding of the RI principles operationalization and its applicability to IS innovations is developed, which can serve to direct further research and guide organizations aiming to enhance their sustainability performance

An Action Design Research Study on Responsible Innovation Teaching and Training for Information Systems Students

While information systems organizations recognize the importance of incorporating responsible innovation into their activities, they often face difficulty implementing this practice because employees lack the required knowledge and skills, particularly the capacity for reflexivity and reflection. This paper aims to accelerate responsible innovation teaching and training for information systems students to expedite the process of positive change for sustainability. Using the action design research methodology and the responsible innovation lens, we developed a workshop enabling information systems students to form measurable reflection skills. The workshop evaluation suggests that learning took place and that students are willing to adopt responsible innovation in their future workplaces. A set of design guidelines is proposed to guide further training programs to enhance students’ ability to address complex challenges responsibly. This paper answers the call for more impactful information systems research to address societal and environmental challenges and enriches the literature on sustainable social development and business practices

途上国における需給動態評価モデルの適用によるコンポストの実現可能性 -モロッコ・ラバト地域のケーススタディ-

東洋大学202

A new revolutionary practice: operaisti and the 'refusal of work' in 1970's Italy

The social protest that engulfed Italy in the 1970s found a theoretical analysis in the work of the operaisti. Through a series of concepts, they outlined a new revolutionary practice that aimed to return to a more authentic reading of Marxism. This article focuses on the notion of ‘refusal of work’ and the ancillary concept of ‘appropriation’ and examines how these theoretical tools emerged out of radical protest in factories and were put forward by the operaisti as a central plank of a revolutionary strategy for the working clas