The project aims to design and develop an artificial intelligence prototype that enhances personalized and adaptive learning experiences for certification exam preparation. Dazia Consulting Inc. seeks to revolutionize educational technology by leveraging AI to tailor learning paths based on individual learner needs and progress. The project will involve creating a prototype that can analyze learner data, adapt content delivery, and provide feedback to optimize exam preparation. The goal is to create a manageable and effective solution that can be completed within a realistic timeframe of 60 hours, spread over 2-8 weeks. This project will allow learners to apply their classroom knowledge in AI and educational technology to a real-world scenario, fostering innovation and practical skills.

Project Scope and Expectations:

During this project, students will research relevant AI techniques, gather and preprocess educational content, develop and train an AI model (e.g., for intelligent question generation, adaptive feedback, or content recommendation), and demonstrate its application in a prototype interface. By clearly defining the scope, students can grasp the project's depth and requirements, ensuring they can meet expectations within the designated timeframe.

Company Overview:

At Dazia Inc., we pride ourselves on our commitment to creating high-quality, impactful educational content and fostering a collaborative, innovative environment. This project offers students an opportunity to gain insights into our company culture, values, and the impactful work we do in improving learning experiences through technology.

Project Details:

Provide more detailed information about the project, breaking down tasks and milestones. This includes:

• Task 1: Data Collection and Preprocessing (Approx. 15 hours)
• Details: Students will identify and collect relevant educational content (e.g., existing Dazia Inc. course materials, open-source certification guides, practice questions). They will then preprocess this data, which may involve text cleaning, tokenization, and formatting to make it suitable for AI model training. This task aims to build a foundational dataset.
• Task 2: AI Model Development & Training (Approx. 30 hours)
• Details: Students will research and select appropriate AI/NLP models (e.g., for question answering, content summarization, or adaptive quizzing). They will then implement and train this model using the prepared dataset. This could involve leveraging existing AI libraries (e.g., spaCy, NLTK, Hugging Face Transformers) and frameworks (e.g., TensorFlow, PyTorch) to build a prototype capable of generating questions, providing intelligent feedback, or recommending study materials.
• Task 3: Prototype Integration & Evaluation (Approx. 15 hours)
• Details: Students will integrate their trained AI model into a basic, user-friendly prototype interface (e.g., a simple web application using Flask/Django or a Python script with command-line interaction). They will then conduct preliminary testing and evaluation of the AI's performance, assessing its accuracy and effectiveness in enhancing the exam preparation process. This involves collecting feedback and identifying areas for future improvement.

The deliverables for this project include a functional AI prototype that demonstrates personalized learning capabilities for certification exam preparation. The team will also provide a comprehensive report detailing the design, development process, and evaluation of the prototype. Additionally, a presentation will be prepared to showcase the prototype's features and potential impact on educational technology. The deliverables will demonstrate the learners' ability to apply AI concepts in a practical setting.

• Deliverable 1: Project Report and Codebase (Word document/PDF & Zipped Code Folder)
• Details: A comprehensive report outlining the methodologies used, AI model architecture, challenges encountered, and results. The report should include insights into the data preprocessing steps, model training process, and evaluation metrics. The accompanying zipped folder will contain all the source code for the AI model and the prototype application, with clear comments and a README.md file for setup and execution instructions.
• Deliverable 2: Prototype Demonstration (Video or Interactive Demo)
• Details: A short video (5-10 minutes) demonstrating the functionality of the AI prototype, showcasing its key features (e.g., adaptive question generation, personalized feedback, content recommendation). Alternatively, an interactive web-based demo (if applicable and feasible for hosting) that allows the Dazia Inc. team to test the prototype directly.
• Deliverable 3: Reflection Note (Word Document/PDF)
• Details: A personal reflection on the learning journey, including insights gained, skills developed, challenges overcome, and suggestions for future iterations of the project. This document allows students to articulate their growth and provide valuable feedback.

For students: To validate the completion of your work, please submit deliverables as uploaded files with a reflection note at the end of your project. These files can take various formats, including Word documents, PDFs, JPEG images, presentations, and more. We request tangible proof or an example showcasing your completed work. If the project involved an Non-Disclosure Agreement (NDA), please provide redacted deliverables or a sample of non-confidential work.

Benefits for Students:

• Develop Advanced AI and NLP Skills: Hands-on experience with real-world applications of machine learning, natural language processing, and data science.
• Exposure to Industry-Relevant Challenges: Gain practical experience in the EdTech sector, understanding how AI can solve real-world problems in education and certification.
• Potential Networking Opportunities: Connect with professionals at Dazia Inc., gaining mentorship and potential future career opportunities in AI, EdTech, or software development.

By refining the project description in this manner, prospective students will have a clearer understanding of the project's scope, time commitment, and its significance within the context of your company. This transparency ensures that students can make informed decisions and approach the project with confidence.

How will you support students in completing the project?

Employer Commitment:

Our company is committed to providing a minimum of 5 hours of mentorship per team. This mentorship will be tailored to address specific project challenges, offer career advice, and foster a supportive learning environment. The mentorship sessions aim to complement the technical guidance and ensure that students not only complete the project successfully but also gain valuable insights into real-world applications of their skills.

This combination of technical support, collaborative tools, and dedicated mentorship hours aims to empower students, fostering an environment conducive to their growth and success in completing the project.