## 7 Future Works  
This research identifies several avenues for future virtual assistant enhancement, focusing on refining accuracy, expanding functionality, and ensuring user-centered application improvements. These recommendations aim to address the current limitations encountered, with the VA being adapted for broader, cross-domain usability. * Mechanisms for Enhanced Context Retrieval: Improvements are identified in refining the retrieval algorithms associated with the Retrieval-Augmented Generation method. The VA could implement adaptive retrieval thresholds that adjust based on query complexity and semantic similarity, resulting in more precise, contextually relevant responses. This enhancement is likely to reduce the inclusion of irrelevant context, which would subsequently minimize hallucinations and increase response accuracy across varied and complex queries. * The capability of the VA to interpret structured data, including tables, course curricula, and schedules, is expected to be expanded, thereby enhancing its functionality in responding to detailed procedural queries. Future work may involve training the VA on datasets focused on structured information, thereby enabling the model to handle tabular and multi-step procedural data effectively. The adaptation would allow for an accurate response to queries that rely heavily on detailed, structured information by the VA.
* The occurrence of hallucinations in enumerative responses is addressed, as it remains a critical challenge in the field. Advanced prompt engineering techniques or response-filtering mechanisms designed explicitly for enumerative tasks may be employed to mitigate hallucinations. The VA is ensured to consistently provide contextually grounded, reliable information, particularly in procedural or multi-step queries that are prone to inaccuracies. * A user feedback mechanism is proposed to facilitate continuous learning. Incorporating a real-time feedback loop within the VA's operational framework is expected to support continuous learning and improvement based on user interactions. User feedback is captured and integrated, particularly in instances of inaccurate or hallucinated responses, allowing for the adaptation of response strategies over time, thereby enhancing the accuracy and reliability of answers. Implementing this user-centric feedback mechanism is expected to be a valuable dynamic model optimization tool. Figure 10: High-Risk vs. Low-Risk Errors. Domain adaptability and cross-industry applications are observed. The VA, while primarily designed for educational environments, is noted for its potential for adaptation across other domains, including healthcare, legal services, and customer support. Tailored data inputs and domain-specific query-handling mechanisms would be required for each field. Future research is suggested to explore adapting the VA's capabilities to meet the unique needs of these fields, thereby establishing it as a versatile tool that can serve diverse professional environments. * Error Detection and Ethical Safeguards: In environments where accuracy is critical, deploying advanced error-detection algorithms and warning systems is suggested to enhance the reliability of the VA. Mechanisms of this nature may be utilized to identify and mitigate the risk of propagating incorrect information, thereby safeguarding against potential misinterpretation by users. Ethical safeguards are essential in applications within sensitive sectors where significant impacts could result from erroneous responses. * Multimodal Interaction Capabilities: The VA's development to support multimodal interactions, including text, voice, and potentially image or video inputs, is expected to result in a more versatile and user-friendly tool. The enhancement is aligned with current trends in artificial intelligence, with the VA being positioned as an accessible and interactive solution that can meet users' needs across various communication preferences.