New Horizons of Science, Technology and Culture Vol. 11

The Role of Artificial Intelligence in Supporting Managerial Decision-Making

Shivani Vats — 2026-06-06

Artificial Intelligence (AI) has emerged as a transformative force in organisational decision-making, fundamentally reshaping how managers process information, evaluate alternatives, and exercise judgment. While early discussions surrounding AI emphasised automation and potential job displacement, contemporary perspectives increasingly highlight its augmentative role in enhancing managerial capabilities. This study presents a comprehensive review of existing literature to examine how AI supports managerial decision-making across diverse organisational contexts. The findings suggest that AI enhances decision quality by improving analytical capabilities, enabling real-time data processing, and supporting complex judgments under uncertainty. However, the effectiveness of AI-assisted decision-making is contingent upon several factors, including managerial trust, perceptions of fairness, transparency, and organisational culture. Challenges such as algorithmic opacity, accountability concerns, and over-reliance on automated systems continue to influence adoption and effectiveness. By synthesising prior research, this paper contributes to a nuanced understanding of human–AI collaboration in managerial contexts and offers theoretical and practical implications for organisations seeking to leverage AI responsibly and effectively.

Machine Learning-Based Detection of Students at Academic Risk Due to Excessive AI Tool Usage

Deepti Sharma — 2026-06-06

The rapid adoption of artificial intelligence (AI) tools in education has significantly transformed learning practices, offering enhanced support for academic tasks such as writing, problem-solving, and research. However, excessive reliance on these tools may negatively impact students’ critical thinking, originality, and overall academic performance. This study proposes a machine learning-based framework to detect students at academic risk due to excessive AI tool usage by analysing behavioural and performance-related data. The research integrates multidimensional inputs, including AI usage frequency, session duration, assignment similarity scores, attendance, and GPA, to develop predictive models for early risk identification. A comprehensive data analysis process involving exploratory data analysis, feature engineering, and model training was conducted using multiple classifiers, including Logistic Regression, Decision Trees, Support Vector Machines, and Random Forest. Among these, the Random Forest classifier demonstrated superior performance, achieving an accuracy of 88%, a precision of 86%, a recall of 83%, an F1-score of 84%, and an AUC of 0.91. Feature importance analysis revealed that AI usage frequency and assignment similarity scores are the most significant predictors of academic risk, highlighting the critical role of behavioural patterns over traditional academic indicators. Correlation analysis further confirmed a negative relationship between AI usage and academic performance, alongside a positive association with content similarity, indicating potential overdependence on AI-generated outputs. This manuscript addresses a critical and emerging challenge in AI-enabled education by examining the impact of excessive AI tool usage on student performance. It contributes to the scientific community by proposing a novel machine learning-based framework that integrates behavioural analytics for early identification of at-risk students. The study provides empirical evidence that AI usage patterns are strong predictors of academic risk, offering new insights for educational data mining research. Additionally, it supports the development of proactive, data-driven interventions to enhance academic integrity and student success.

The findings emphasise the dual-edged nature of AI in education, where its benefits must be balanced with responsible usage. The proposed framework enables early detection of at-risk students and supports data-driven, proactive intervention strategies to enhance academic integrity and student success. This study contributes to the growing field of educational data mining by demonstrating the effectiveness of machine learning in monitoring emerging learning behaviours in AI-assisted environments.

A Critical Review of Artificial Intelligence and Its Influence on Organisational Work Practices and Culture

Disha Grover — 2026-06-06

Artificial intelligence (AI) is reshaping the landscape of organisational life with a speed and breadth unprecedented in the history of technological change. This critical narrative review synthesises peer-reviewed evidence published between 2018 and 2026 to examine the multidimensional ways in which AI influences organisational work practices and culture. Drawing on 35 verified scholarly sources, the article investigates four interrelated domains: the transformation of task structures and labour processes through automation and augmentation; the emergence of algorithmic management and its implications for worker autonomy and organisational control; the cultural shifts accompanying AI adoption, including changes to trust, learning, and leadership; and the ethical tensions arising from AI's deployment in human resource management and decision-making. The review reveals that AI does not operate as a neutral technology; rather, its effects are profoundly contingent on organisational context, governance choices, and the degree to which employees are meaningfully involved in implementation. Whereas AI creates measurable productivity gains and enables novel forms of human–machine collaboration, evidence equally points to deepening workplace inequalities, surveillance risks, and the erosion of meaningful work for certain categories of employee. Critically, organisations that attend solely to technical deployment while neglecting cultural readiness and ethical governance consistently fail to realise the anticipated value of AI investment. The article concludes by outlining an agenda for future research, highlighting the need for longitudinal, contextually sensitive, and worker-centred scholarship to inform both management practice and public policy.