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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Mustapha Hain
University Hassan II
Computer Science & IT Engineering

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Artificial intelligence-based lead propensity prediction Aissam Jadli; Mustapha Hain; Anouar Hasbaoui
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v12.i3.pp1281-1290

Abstract

Lead propensity prediction is a data-driven method used to define the value of prospects, by assigning points to them based on their engagement with the business's digital channels, based on multiple key attributes correlating to their attraction to the proposed services or items. The resulting score is closely related to the financial worth of each lead and may be revealing its position in the buying cycle. The marketing teams can then focus on generated leads and prioritize the most prominent ones to improve the conversion rates, using the assigned score on the lead scoring step. The authors investigated using a combination of a data-driven approach and Artificial intelligence (AI) techniques for the lead-scoring process. The experimentation shows that the random forest (RF) is the most suitable model for this task with an accuracy score of 93.04% followed by the decision tree (DT) model of 91.47%. In contrast, when considering the training time, DT and logistic regression (LR) needed a shorter time to learn from the dataset while maintaining decent performances. In contrast, these models represent promising alternatives to the RF model especially in the case of a huge volume of transactions and prospects or in a big data context. 
Navigating the new frontier: large language models and their implications for education Laila Boullous; Mustapha Hain; Adil Chergui; Brahim Elbhiri
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 3: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i3.pp2141-2152

Abstract

This survey characterizes the contributions of large language models (LLMs) to technology enhanced learning by relating their capabilities to actual educational functions, making comparisons with traditional models of language. The contributions for this study are: i) introduce an education centered taxonomy that classifies LLM use by four key functions personalization and adaptivity, assessment and evaluation, profiling and prediction, and intelligent tutoring with illustrations from deployed systems and tools; ii) give a domain-based comparison of where LLMs outperform traditional models (sentiment analysis with sarcasm, context-aware question answering, and abstractive summarization) and why those advantages will mean something to e-learning practice; iii) synthesize six cross-cutting risks, including computational cost/carbon, privacy, bias and hallucination, labor displacement, interpretability, and the limits of human-like judgment, and provide practical design/research implications; and iv) report on a transparent review protocol that got the initial corpus down to 50 key articles, allowing for modifications and future updates from other interested researchers. In sum, the discussion about LLMs in education has been pushed past the broad strokes to a situation where there is a comprehensive vocabulary for what LLMs can do, and how they may or may not responsibly improve learning experiences, educator workflows, and systems/learning design in e-learning.
Co-Authors Adil Chergui Aissam Jadli Anouar Hasbaoui Brahim Elbhiri Laila Boullous