Iris identification is a well‐known technology used to detect striking biometric identification procedures for recognizing human beings based on physical behaviour. The texture of the iris is unique and its anatomy varies from individual to individual. As we know, the physical features of human beings are unique, and they never change; this has led to a significant development in the field of iris recognition. Iris recognition tends to be a reliable domain of technology as it inherits the random variation of the data. In the proposed study of approach, we have designed and implemented a framework using various subsystems, where each phase relates to the other iris recognition system, and these stages are discussed as segmentation, normalisation, and feature encoding. The study is implemented using MATLAB where the results are outcast using the rapid application development (RAD) approach. We have applied the RAD domain, as it has an excellent computing power to generate expeditious results using complex coding, image processing toolbox, and high‐level programing methodology. Further, the performance of the technology is tested on two informational groups of eye images MMU Iris database, CASIA V1, CASIA V2, MICHE I, MICHE II iris database, and images captured by iPhone camera and Android phone. The emphasis on the current study of approach is to apply the proposed algorithm to achieve high performance with less ideal conditions.

This paper presents the design of a personalized learning agent powered by the Agentic RAG technique. The agent can interpret learners’ queries and autonomously decide which tools should be used to generate the most suitable response. When the learner shares an Open Educational Resource (OER) they wish to learn from, the agent first breaks the content into smaller, manageable chunks. These chunks are then indexed sequentially to preserve the natural flow of the text. At the same time, chunks are also converted into vector embeddings that allow semantic retrieval. Depending on the learner’s request, different tools are selected by the agent. For example, when the learner requests learning aids like summaries, quizzes, or flashcards, the agent invokes the corresponding tool. This tool passes the sequentially indexed chunks to a small language model to generate the output. For context-specific queries, another specialized tool that relies on vector indexing and retrieval-augmented generation (RAG), is invoked. Visual question answering is handled by a separate tool that leverages multimodal RAG using a multimodal small language model. This agentic setup improves the accuracy and relevance of responses generated by the agent. To test its agentic behaviour, we probed our agent with a diverse set of questions drawn from four different OERs. We thoroughly examined each response and tracked the tools that got invoked autonomously. We also compared the similarity of summaries produced by our agent against those generated by ChatGPT (GPT-4o) using BERT Score as the evaluation metric. Our findings indicate that the agent consistently selected the appropriate tools and the summaries generated by our agent showed close semantic similarity to those produced by GPT-4o, suggesting that the proposed approach can provide performance reasonably close to a state-of-the-art model. The agent being lightweight resides on learner’s local machine and avoid dependence on cloud-based AI ensuring the privacy of learner’s data. It is affordable as it entirely relies on open source frameworks and small models. As the agent provides personalized support to learners by answering their context-based queries and providing on-demand learning aids, it improves their engagement with the educational content. This research shows that designing agentic AI tools using open-source software to address diverse learning needs is technically and economically feasible as well as educationally valuable.