TIM-6072 v1

Katona, J., & Gyonyoru, K. I. K. (2025). AI-based Adaptive Programming Education for Socially Disadvantaged Students: Bridging the Digital Divide. TechTrends: Linking Research and Practice to Improve Learning A Publication of the Association for Educational Communications & Technology, 1–18. https://doi.org/10.1007/s11528-025-01088-8

This paper examines how AI adaptive programming education helps bridge the digital divide for socially disadvantaged students. The article highlights the importance of proficiency in the digital economy and examines barriers such as limited access to technology and educational resources. The authors indicate that AI learning environments improve programming knowledge and engagement across behavioral, emotional, and cognitive dimensions. The study suggests that AI adaptive learning enhances educational equity by tailoring content to individual needs, providing real-time feedback, and fostering better learning outcomes for disadvantaged students.

D. S. Dewanthi, H. Kristopo, N. N. Qomariyah and M. Axel, "AI Recommendations: Friend or Foe? Unraveling the Impact on Consumer Benefits and Privacy," 2024 International Conference on ICT for Smart Society (ICISS), Bandung, Indonesia, 2024, pp. 1-8.

This study explores the impact of AI systems on consumer behavior and privacy in e-commerce. It highlights how recommendation quality and transparency influence purchasing decisions and trust in online platforms. The study reveals consumers prefer recommendations aligning to their preferences and those that are visually engaging, leading to positive shopping experiences. Concerns about data privacy persist, as users are hesitant to share sensitive information despite trusting platforms with personal details.

Naik N, Hameed BMZ, Shetty DK, Swain D, Shah M, Paul R, Aggarwal K, Ibrahim S, Patil V, Smriti K, Shetty S, Rai BP, Chlosta P and Somani BK (2022) Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Front. Surg. 9:862322. doi: 10.3389/fsurg.2022.862322

This study explores legal and ethical challenges regarding AI integration in healthcare, including accountability and responsibility. The article highlights concerns related to privacy, algorithmic bias, and the lack of clear regulatory frameworks governing AI medical decisions. While AI enhances diagnostic accuracy and operational efficiency, it raises questions about liability in cases of errors or adverse outcomes and emphasizes the need for transparent governance, ethical oversight, and interdisciplinary collaboration.

Ebert, C., Panyam, S., & Gujar, P. (2025). AI for Cloud and SaaS: Technologies and Business Models. Computer, 58(4), 100–105.

This article examines how AI is transforming cloud services and Software as a Service (SaaS) business model. It discusses AI-native products, adaptive cloud fabrics, and enhanced tracking and observability as key drivers of change. The research highlights how AI optimizes SaaS platforms by improving automation, security, and predictive analytics, enabling businesses to scale efficiently. Additionally, the paper discusses monetization strategies, including AI subscription models and performance-based pricing.