With the rapid evolution of online education, the demand for personalized learning experiences has grown significantly. This paper introduces an innovative approach to enhance e-learning platforms through the integration of a machine learning-based framework. The proposed system focuses on optimizing the Recommender System, leveraging Recommender Systems techniques to tailor educational content to individual learner profiles. Through........

Keywords- Recommender Systems, E-Learning, Recommender System, Dynamic Adaptation, Real time Learning.

[1]. Imran, H., Belghis-Zadeh, M., Chang, T. W., Kinshuk, & Graf, S. (2016). PLORS: A personalized learning object recommender system. Vietnam Journal of Computer Science, 3, 3–13. https://doi.org/10.1007/s40595-015-0049-6
[2]. George, G., & Lal, A. M. (2019). Review of ontology-based recommender systems in e-learning. Computers & Education, 142, 103642.
[3]. Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020b). Vision, challenges, roles and research issues of artificial intelligence in education. Computers & Education: Artificial Intelligence, 1, 100001.
[4]. Raj, N. S., & Renumol, V. G. (2021). A systematic literature review on adaptive content recommenders in personalized learning
environments from 2015 to 2020. Journal of Computers in Education. https://doi.org/10.1007/s40692-021-00199-4
[5]. Zhou, Y., Huang, C., Hu, Q., Zhu, J., & Tang, Y. (2018). Personalized learning full-path recommendation model based on LSTM neural networks. Information Sciences, 444, 135–152.

The general public seldom acknowledges job description bias because it remains widely unrecognized as it seriously affects both candidate variety and hiring procedure inclusivity. The research has developed Explainable AI for Trustworthy recruitment as an Artificial Intelligence system based on modern Natural Language Processing approaches to detect unintended biases in job listings. Various traditional tools based on keyword matching differ from the Explainable AI for Trustworthy recruitment system because it uses RoBERTa transformer model with contextual understanding to discover subtle intersectional biases which include gender
and racial as well as age and disability aspects. The system identifies discriminatory wording to enable recruiters in writing job descriptions free of prejudice. The Explainable AI system for Trustworthy recruitment enhances both recruitment system effectiveness and universal hiring reach as well as working toward diverse workplace diversity.

Keywords-Bias Detection, Job Descriptions, RoBERTa, BERT, Multi-label Classification, Explainable AI (XAI), SHAP, Circumstantial Embeddings, Intersectional Bias, Trustworthy recruitment Practices, NLP, Transformer Models.

[1] K. Wilson and A. Caliskan, "Gender, race, and intersectional bias in resume screening via language model retrieval," Proc. AAAI/ACM Conf. on AI, Ethics, and Society (AIES), Seattle, WA, USA, 2024.
[2] G. C. Nomelini and C. B. Marcolin, "Gender bias in large language models: A job postings analysis," Revista de Administração
Mackenzie, vol. 25, no. 6, pp. 1–27, 2024.
[3] M. Dikshit, H. Bouamor, and N. Habash, "Investigating gender bias in STEM job advertisements," Proc. Workshop on Gender Bias in NLP (GeBNLP), 2024.
[4] T. Kanij, J. Grundy, and J. McIntosh, "Enhancing understanding and addressing gender bias in IT/SE job advertisements," J. of
Information and Software Technology, 2024. networks. Information Sciences, 444, 135–152.

The rapid advancement of artificial intelligence (AI) has opened new avenues for innovation in retail, particularly in the domain of grocery management. This paper presents a Smart Grocery Ordering System that utilizes AI-driven predictive analytics and personalized health recommendations to optimize the shopping experience. The proposed system analyzes individual consumption behavior using machine learning techniques to forecast future grocery needs, automate routine ordering, and provide nutritional guidance tailored to specific medical conditions such as diabetes and hypertension.......

Keywords- Artificial intelligence, grocery automation, food waste reduction, health-based recommendations, machine learning, predictive analytics, personalized retail.Top of Form.

[1] Srinivas Donthamsetti and R.K. Jain, "Study of consumer buying behavior for online Grocery shopping," 2023.
[2] Dr. Deepshikha Aggarwal, "Exploring the Role of AI for Online Grocery Shopping through Enhancing Personalized

Recommendations and Customer Segmentation," Technoarete Transactions on Advances in Computer Applications (TTACA) Vol-
2, Issue-3, vol. 2, no. 3, p. 5, September 2023.

[3] Faisal Sulaiman A. Alghafri and Mohammed J. Yousif Abdulhaseeb H. abdulmalik, "Smart Online Grocery Shopping App
Development," Artificial Intelligence & Robotics Development Journal, vol. 1, no. 2, p. 10, April 2021.
[4] Yerra Sankar Rao and Suchismita Parida, "Grocery Shopping Based on AI Technology," International Journal of Engineering,
Management, Humanities and Social Sciences Paradigms (IJEMHS), vol. 30, no. Special, p. 6, October 2018.

[5] Pritam Mahapatra, Maheswar Banerjee and Dr.Tapas kumar Nandi Soumyojit Paul, "AI SHOPPING SOLUTION - The Smartest All- in-One ," in ICAMEI 2021 Materials Science and Engineering, 2021, p. 11.