Report about a Lecture delivered by Professor Abdelaziz El Hammouchi at Bitlisaniyat International Academy:
This lecture was held on Sunday January 25th, 2026, from 7:00 PM to 9:00 PM online on the Bitlisaniyat International Academy platform (Turkey), under the title “Beyond Lecture Hall: Integrating Generative AI into Higher Education Curricula ”
The lecture began with a welcoming address by Professor Messoudi Tarik, who greeted all attendees, including the presenter Professor Abdelaziz El Hammouch, the coordinator Hmad Benaissa, and the rapporteur Ahmed Mertou. The moderator then introduced the guest speaker by presenting a brief biography, as he is an adjunct lecturer and researcher at the University of Sidi Mohamed Ben Abdellah, Fes, Morocco. His academic activities centre on educational development, interdisciplinary research, and advancing scholarly collaboration across Moroccan institutions. He has contributed to various research projects aimed at improving academic quality and fostering innovation in higher education. He is dedicated to supporting curriculum advancement and student learning through rigorous inquiry and publication. Then the professor took the floor and thanked the organizing committe before starting his talk.
Introduction
The lecture addressed the growing role of Generative Artificial Intelligence (AI) in higher education, with a particular focus on its pedagogical, linguistic, and ethical implications. The professor aimed to move beyond fear-driven narratives surrounding AI and instead highlight its potential as a transformative tool for teaching, learning, and academic research.
Historical Background of Artificial Intelligence:
The lecture began with an overview of the historical development of AI. The professor traced its origins back to 1950, referring to Alan Turing’s work and the famous Turing Test, which marked an early attempt to define machine intelligence. He then highlighted 1956 as a key milestone, noting the Dartmouth Conference where the term Artificial Intelligence was officially coined.
The discussion moved forward to 2010, which marked the emergence of the deep learning boom, driven by the availability of big data, increased computational power, and the use of GPUs. The most significant recent milestone was identified as 2022, with the launch of Generative AI tools such as ChatGPT, which differ from earlier AI systems by their ability to generate new content rather than merely retrieve existing information.
Definition of AI and Large Language Models:
AI was defined as a branch of computer science dedicated to creating systems capable of performing tasks that typically require human intelligence. Within this framework, the professor introduced Large Language Models (LLMs), explaining that they function by predicting the next word or token based on probability. This process relies on machine prediction, massive training data, and a large number of parameters
How Large Language Models Learn:
The professor then explained how LLMs learn, outlining three main stages. The first is the pre-training phase, where models are exposed to vast amounts of textual data. This is followed by fine-tuning, which refines the model for specific tasks. Finally, Reinforcement Learning from Human Feedback (RLHF) is applied to align the model’s outputs more closely with human expectations and values.
How AI “Thinks” and the Issue of Hallucination
A critical point emphasized in the lecture was that AI does not think or understand in a human sense. Instead, it operates as a probability engine, not a search engine or a knowledge engine. This explains why AI systems sometimes produce incorrect or fabricated information, a phenomenon known as hallucination.
AI and Applied Linguistics:
The lecture highlighted strong connections between AI and applied linguistics. AI can be extensively used in linguistic and educational research by providing personalized feedback during the learning process. Areas such as linguistics, pragmatics, and discourse analysis were identified as fields that could particularly benefit from AI integration. This allows teachers to focus on developing learners’ higher-order thinking skills, while lower-level cognitive tasks can be supported by AI tools in a pedagogically sound manner.
Control and Ownership of AI:
The professor clarified that AI did not emerge randomly or independently. Its development has been largely driven by major technology companies such as Google and Microsoft, as well as by open-source communities, which play a crucial role in innovation and accessibility.
AI and Moroccan Higher Education
The lecture also addressed the Moroccan context. Morocco, according to the professor, is not merely observing global AI developments but has launched national strategies such as PACTEESRI 2030. Initiatives like Code 212 centers and digital soft skills programs demonstrate efforts to integrate AI into higher education. However, challenges remain, particularly in terms of infrastructure and language bias
Prompt Engineering:
Prompt engineering was presented as a new and essential literacy skill for students and educators. The professor emphasized that the quality of AI output depends heavily on the quality of user input. He introduced a practical framework for writing effective prompts: Persona – Task – Context, which helps guide AI responses more accurately and meaningfully.
AI Tools used :
Several AI tools were discussed during the lecture. Google Gemini was highlighted as a powerful multimodal tool capable of analyzing different types of input and reducing hallucination. NotebookLM was presented as an example of grounded AI, as it works exclusively with user-provided sources, thereby minimizing source invention and hallucination.
AI in Academic Research:
In the context of academic research, the professor discussed AI applications in literature reviews, data analysis, and referencing. Tools such as Consensus, Elicit, and Research Rabbit were identified as useful for facilitating literature review processes. For data analysis, AI can support both qualitative and quantitative research. However, for referencing and citation, the professor cautioned against relying on AI to generate sources, recommending tools like Zotero and Mendeley for accuracy and reliability.
Redefining Plagiarism in the Age of AI:
A major part of the lecture focused on redefining plagiarism. The professor distinguished between AI-assisted use, AI co-piloting, and AI-generated content, noting that plagiarism occurs when users copy and paste AI-generated content without critical engagement. Detection tools such as Turnitin and iThenticate were discussed, with an explanation of how they rely on statistical measures like perplexity and burstiness, focusing on writing style rather than meaning or sources.
The professor also highlighted the limitations of these tools, including bias against non-native English speakers and the ease with which detection can be bypassed through paraphrasing. As a result, he emphasized the need for institutional responses, including strong infrastructure, proper training, and clear ethical charters.
Conclusion
In conclusion, the professor stressed that AI cannot replace teachers but should be viewed as an assistant and supplementary tool. While challenges related to academic integrity remain, the opportunities offered by AI in higher education are numerous. The future of AI, he argued, is not about deciding whether to use it, but about learning how to use it responsibly to enhance critical digital literacy. The lecture ended with an open discussion session, during which participants engaged in informative questions and meaningful interaction.
Ahmed Mertou
تعليق