AI-driven education platforms have made some progress in personalisation, yet most remain constrained to static adaptation—predefined quizzes, uniform pacing, or generic feedback—limiting their ability to respond to learners’ evolving understanding. This shortfall highlights the need for systems that are both context-aware and adaptive in real time. We introduce PAL (Personal Adaptive Learner), an AI-powered platform that transforms lecture videos into interactive learning experiences. PAL continuously analyzes multimodal lecture content and dynamically engages learners through questions of varying difficulty, adjusting to their responses as the lesson unfolds. At the end of a session, PAL generates a personalized summary that reinforces key concepts while tailoring examples to the learner’s interests. By uniting multimodal content analysis with adaptive decision-making, PAL contributes a novel framework for responsive digital learning. Our work demonstrates how AI can move beyond static personalization toward real-time, individualized support, addressing a core challenge in AI-enabled education.

Large language models (LLMs) are increasingly attracting the attention of healthcare professionals for their potential to assist in diagnostic assessments, which could alleviate the strain on the healthcare system caused by a high patient load and a shortage of providers. For LLMs to be effective in supporting diagnostic assessments, it is essential that they closely replicate the standard diagnostic procedures used by clinicians. In this paper, we specifically examine the diagnostic assessment processes described in the Patient Health Questionnaire-9 (PHQ-9) for major depressive disorder (MDD) and the Generalized Anxiety Disorder-7 (GAD-7) questionnaire for generalized anxiety disorder (GAD). We investigate various prompting and fine-tuning techniques to guide both proprietary and open source LLMs in adhering to these processes, and we evaluate the agreement between LLM-generated diagnostic outcomes and expert-validated ground truth. For fine-tuning, we utilize the MentaLLaMa and Llama models, while for prompting, we experiment with proprietary models like GPT-3.5 and GPT-4o, as well as open source models such as llama-3.1-8b and mixtral-8x7b. Software Availability . We make all software artifacts available at this GitHub link ( https://github.com/kauroy1994/Large-Language-Models-for-Assisting-with-Mental-Health-Diagnostic-Assessments ). Institutional Review Board (IRB) . This study does not require approval from the IRB. It involves using clinician-annotated social media posts, authorized for research purposes. The primary objective is to evaluate the effectiveness of LLMs that incorporate diagnostic criteria for major depressive disorder and general anxiety disorder for assisting with mental health assessments.

As the demand for healthier, personalized culinary experiences grows, so does the need for advanced food computation models that offer more than basic nutritional insights. However, current food computation models lack the depth to provide actionable insights like ingredient substitution or alternative cooking actions to suit users’ dietary goals. To address this, we introduce and demonstrate Pic2Prep, a multimodal conversational system that generates detailed cooking instructions, actions and ingredient lists from both images and text provided by users. The system is developed using a novel dataset generated through Stable Diffusion, where the input consists of recipe titles and ingredient lists from the Recipe1M dataset to create synthesized food images with variations. This dataset is used to fine-tune the Bootstrapping Language-Image Pre-training (BLIP) model to extract cooking instructions and ingredients from food images. Pic2Prep also employs the CookGen model, a small-scale custom generative model to derive specific cooking actions from cooking instructions. A custom mapper, trained on the Mistral model, links these actions to the corresponding ingredients, creating a comprehensive understanding of the cooking process. The system features an interactive user interface that allows users to input images and ask targeted questions, receiving real-time responses.