Create a gantt chart for the timeline below

Needs Assessment $($Week $1)$: The University of Lusaka conducts a comprehensive needs assessment to identify areas where AI can be beneficial. This involves gathering inputs from faculty, students, and administrative staff to determine specific challenges and opportunities for AI integration. Conducting a needs assessment is essential for the planning of a project $($Schwalbe$,2019).$

Initiation and Planning $($Week $2)$: The project team is formed, including faculty, students, and external experts if necessary. The objectives and scope of the AI project are defined and aligned with the university's goals. A detailed project plan is developed, outlining timelines, resources, and budget. Key stakeholders are identified, and a communication plan is established.

Research and Selection of AI Tools $($Weeks $3-4)$: The project team researches various AI tools and platforms suitable for educational purposes. It conducts a literature review to understand current AI technologies and methodologies. Data requirements are identified, and data sources are secured, ensuring compliance with privacy regulations. Initial data collection begins, focusing on quality and relevance to the project objectives. They evaluate options based on functionality, cost, and compatibility with existing systems, ultimately selecting the most appropriate tools for implementation.

Developing an Implementation Plan $($Week $5)$: The team creates a detailed implementation plan that outlines objectives, timelines, resource allocation, and responsible parties. This plan serves as a roadmap for the project, ensuring all stakeholders are aligned, as the project roadmap ensures clarity and alignment $($Kerzner$,2017).$

Pilot Program Launch $($Weeks $6-7)$: A pilot program is launched in select departments to test the AI tools in a controlled environment. The AI model architecture is designed, considering the project's specific needs. Data preprocessing and cleaning are performed to prepare for model training. The team develops the AI model using appropriate algorithms and tools. Initial testing of the model is conducted to ensure functionality. Faculty and students are trained on how to use the tools effectively, and feedback is collected to assess usability and effectiveness.

Evaluation and Adjustment $($Weeks $8-9)$: The project team evaluates the pilot program's outcomes, gathering data on user satisfaction and performance improvements. Based on this feedback, necessary adjustments are made to the AI tools and training materials. The AI model undergoes rigorous testing using validation datasets. Performance metrics are evaluated, such as accuracy, precision, and recall. The model is refined based on test results, optimizing for better performance. Feedback from stakeholders is gathered to ensure alignment with expectations.

Full$-$Scale Implementation $($Weeks $10-11)$: After refining the tools and processes, the university rolls out the AI systems across all departments. This phase includes extensive training sessions for faculty and staff to ensure everyone is equipped to utilize the new technology.

Monitoring and Support $($Weeks $12-14)$: The project team monitors the implementation closely, providing ongoing support and troubleshooting as needed. They establish feedback mechanisms to continuously gather input from users, ensuring that the AI systems meet the evolving needs of the university community.

Deployment and Integration $($Weeks $15-18)$: The AI model is deployed in a controlled environment within the university. Integration with existing systems and processes is carried out. Training sessions are conducted for end$-$users to facilitate smooth adoption. Initial monitoring of the model's performance in the real$-$world setting begins.

Evaluation and Iteration $($Weeks $19-22)$: The project's impact is assessed against the initial objectives and key performance indicators. Key Performance indicators measure the project$$s success $($Hillson$,2017).$ Feedback from users and stakeholders is collected for further improvements. Necessary adjustments and iterations are made to enhance the model's effectiveness. Documentation of the project process and outcomes is completed. The project concludes with a comprehensive evaluation of the AI integration process. The team compiles a report detailing successes, challenges,

Closure and Future Planning $($Weeks $23-24)$:

A final project report is prepared, summarizing achievements, challenges, lessons learned, and recommendations for future AI initiatives, presenting it to university leadership for review. A presentation is made to stakeholders, highlighting the project's success and potential areas for expansion. Plans for scaling the AI solution or initiating new projects are discussed. The project is formally closed, and team members are recognized for their contributions.

This structured approach ensures that the implementation of AI is systematic, well$-$supported, and aligned with the university's aim and strategic goals. It further, allows for thorough development, testing, and integration of AI within the university setting.