Artificial Intelligence Founda#ons CIS4049-N In-course Assessment Overview of Requirements Assessment for Ar#ficial Intelligence Founda#ons (CIS4049-N) requires you to implement the

Artificial Intelligence Founda#ons

CIS4049-N

In-course Assessment

Overview of Requirements Assessment for Ar#ficial Intelligence Founda#ons (CIS4049-N) requires you to implement the Artificial Intelligence (AI) techniques to a case study of your choice and cri<cally evaluate the selec<on, implementa<on, and experimental value of the results.

The implemented AI solutions will be assessed by one in-course assessment consisting of the following:

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• A written report with a word limit of 4,000 words and an artifact or examples. Your report should inves<gate and document the implementation <on of AI techniques in a real-world case study of your choice, where you are required to cri<cally evaluate and reflect on the selec<on and the applica<on of AI techniques, jus<fy the u<lisa<on of these techniques, and experimentally validate the results. The artefact consists of either a single AI solution or a porMolio of work (usually 2-3), demonstra<ng applica<on of the AI techniques to one or a small collec<on of real-world case studies chosen by students (see sec<on ‘Requirements for the AI solu<on’ for further details). Moreover, students must produce brief voice-over walkthrough video (between 2 and 5 minutes), showing and demonstrating what has been done in the ICA. The student is expected to introduce his work and discuss what has been achieved. It is also recommended that the student highlights the issues and limitations encountered during implementa<on [100 points].

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Further details are given below, and there will be a suppor#ng briefing session on the ICA.

Submission of materials must be made via Backboard to the link provided. The submission date is specified in the submission schedule.

Requirements for the AI solution

Your assessment requires you to produce a written report and a walkthrough video:

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ICA-SPECIFICATIONS-CIS4049-N-2023-2024

AI solu#on (report equivalent to 4,000 words and walkthrough video) [100 points]

Design and implement AI techniques in a real-world case study of your choice. Provide a reflection on your module experience and how you met the in-course assessment requirements. Your report should document your learning and personal development, providing evidence (e.g., screenshots or images of prac<cal or in-course assessment work) where appropriate to support your solu<on. It would be best if you concentrated on what you learned and how your knowledge and skills developed as you addressed the in-course assessment and module content. Document the challenges you encountered and what you did to resolve them. You could also consider how your experience may affect your future studies and employment options or choices. You could also design a personal development learning plan based on your self-evaluation <. This should be in the form of an MS Word or PDF document or an alternative document in a readable format. The artifact consists of either a single AI solution or a portfolio of work (usually 2-3), demonstra< application of the AI techniques to one or a small collec< of real-world case studies chosen by students (see sec<on ‘Requirements for the AI solu<on’ for further details). Moreover, you will also upload the file containing all the source code of your solu<on (e.g., the .r and .py file(s)), and please submit the other files used for your experiments in a readable format.

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The student must also produce a voice-over brief walkthrough video (2-3 minutes), showing and demonstrating what has been done in the implemented AI solution. The student is expected to introduce his work and discuss what has been achieved. It is also recommended that the student highlights the issues and limitations encountered during implemention.

This in-course assessment will meet all the learning outcomes: PTS1, PTS2, PTS3, RKC1, RKC2, RKC3, PS1, and PS2.

Learning Outcomes

Personal and Transferable Skills (PTS)

PTS 1. Effec<vely communicate and evaluate complex information related to AI theory.

PTS 2. Use personal reflection to analyse self and own ac<ons while working as a group or individual in a real-world AI case study.

PTS 3. Reflect upon, take ownership of, and critically appraise the outcome of an implemented solution against a given brief for a simulated or real-world problem using appropriate AI technologies.

Research, Knowledge, and Cogni#ve skills (RKC):

 

 

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RKC 1. Cri<cally analyse a solution to a given real-world case study.

RKC 2. U<lise effec<vely AI software and techniques in problem-solving.

RKC 3. Cri<cally appraise recent scien<fic literature in AI in a given scenario.

Professional skills (PS):

PS 1. Cri<cally evaluates the commercial risks and opportunities related to solving an AI problem.

PS 2. Autonomously evaluate improvements to performance drawing on innova< or best practices in applied AI-related skills.

Outline Marking Scheme

Your submission will be assessed according to the following criteria:

1. Systema<c review of relevant science <fic literature [40 points]. 2. Cri<cal evalua<on and discussion of the significance of the application of AI techniques [40

points]. 3. Coverage of relevant commercial risks and professional issues [20 points].

Below is a provisional indica< of the criteria applied to determine points for each element.

Please note: Excep&onally, while points are allocated to specific parts, outstanding work in one area may be used to trade-off points against poorer work in another area.

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Review of Scien#fic Literature

[40 points]

Concerns a critical analysis of the scientific literature in the real-world case addressed, and whether the implementation of the AI solution derives from this thorough evaluation.

Excellent 70% and above

The implemented AI solution derives from an extremely thorough evaluation and review of the scientific literature. The implemented solution is well connected with the related scien<fic literature. The produced walkthrough video demonstrates the excellent understanding of the scientific literature, related to the chosen case study.

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Very Good 60%-69%

The implemented AI solution correctly models the addressed real-world case mee,ts all requirements, and performs consistently as intended. Nevertheless, there are some missing references to the science <fic background. The produced walkthrough video demonstrates a very good understanding of the scientific literature related to the chosen case study.

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ICA-SPECIFICATIONS-CIS4049-N-2023-2024

Sa<sfactory 50-59%

Sa<sfactory implementa<on of the AI solu<on modeling the real-world problem, even though the AI techniques adopted are not in line with the science <fic literature. Nevertheless, the implemented solution and code meet a good proportion of the requirements. The produced walkthrough video demonstrates a sa<sfactory understanding of the scientific literature related to the chosen case study.

Fail

Less than 50% Insufficient. The proposed solution is not supported by any scientific literature. The produced walkthrough video demonstrates a poor understanding of the scientific literature related to the chosen case study.

NS NON-SUBMISSION N/A

Evalua#on and discussion of the

significance of the application of AI

techniques [40 points]

Concerns whether the AI solution derives from a cri&cal evaluation and discussion of the AI techniques in a real-world case study, and if the implemented solution is significant.

Excellent 70% and above

The proposed AI solution demonstrates a thorough evaluation, discussion, and correct application of AI techniques in the real-world case study. The AI solution operates without fatal error at run <me and fully sa<sfies the real-world case requirements. The solution meets all requirements and performs consistently as intended. The code is clear, with enough comments. Of the scien<fic literature related to the chosen case study. The produced walkthrough video shows an excellent understanding of the implemented AI solution.

Very Good 60%-69%

The proposed AI solution demonstrates a very good evaluation, discussion, and correct application of AI techniques in the real-world case study. The implemented AI solution operates at run-<me without fatal errors with some minor logic errors. The solution meets all requirements and performs consistently as intended. The code is not clear since there are not enough comments. The produced walkthrough video shows a very good understanding of the implemented AI solution.

Sa<sfactory 50-59%

The proposed AI solution demonstrates a sa<sfactory evaluation, discussion, and application of AI techniques in the real-world case study. The implemented AI solution fails to operate or contains mistakes that cause it to crash under certain conditions during the run-<me but contains evidence of the ability to employ fundamental techniques to design an AI solution. The produced walkthrough video shows a satisfactory understanding of the implemented AI solution.

 

 

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Fail Less than 50%

Insufficient. The proposed AI solution does not satisfy the minimum requirements.

NS NON-SUBMISSION N/A

Coverage of relevant commercial risks and professional issues

[20 points]

Concerns the reflec&on on the relevant commercial risks and professional issues of the implemented AI solution.

Excellent 70% and above

There is extensive commentary on the relevant commercial risks and professional issues associated with the implemented AI solution, demonstrated by a cri<cal and elaborated discussion. The provided walkthrough video shows an extensive and cri<cal reflec< on relevant commercial risks and professional issues.

Very Good 60%-69%

Substan <al commentary on the relevant commercial risks and professional issues of the implemented AI solu< demonstrated by a consistent discussion. The producedugh video shows a substan<al reflec<on revant commercial risks and professional issues.

Sa<sfactory 50-59%

There is some useful commentary about the relevant commercial risks and professional issues of the implemented AI solution and the whole learning experience of the ICA and studying the module. Nevertheless, it tends to be described rather than reflected and needs to concentrate more on personal evaluation and development. The submitted report would benefit from including more supporting evidence (e.g., schema<cs, screenshots, research, etc.). It could also benefit from identifying more learning needs and how they might be addressed. The provided walkthrough video shows a satisfactory reflection < on relevant commercial risks and professional issues.

Fail Less than 50%

Insufficient. Any reference to the implemented AI solution’s relevant commercial risks and professional issues.

NS NON- SUBMISSION

N/A

 

 

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Deliverables & Submission

You must submit your work to Blackboard via the assessments link by the due date. Regarding submitting your AI solution, you will upload a report (equivalent to 4,000 words) that should document the AI solution and your reference. Also, in this case, you are free to choose the <tle of your report, but do not always forget to include your student ID, name, and surname: “studentID_lastname_firstname_<tle_of_your_report.docx”.

Moreover, you will also upload the R or Python file containing all the source code of your solu< (e.g., the .r and .py file(s)). Please label this file with your student ID, name, and surname: “studentID_lastname_firstname.r (or .py)” or, alterna<vely, if you prefer, you can also include the name of your solu<, so: “studentID_lastname_firstname_name_of your_solu<on.r (or .py)”. Please also submit the other files used for your experiments in a readable format.

Moreover, you will upload a voice-over brief walkthrough video (2-3 minutes), showing and demonstrating what has been done in the next parts of the project. The student is expected to introduce his work and discuss what has been achieved. It is also recommended that the student highlights the issues and limitations encountered during implementation <.

You may use a zip file for packaging your submission artifacts (i.e., the zip file containing your report, the source code, all the other files used for your experiments, and the walkthrough video).

All the submitted files within the zip file should be labeled as follows for iden<fica<on purposes:

studentID_lastname_firstname.zip (e.g. x1234567_smith_jane.zip)

Your report, the source code of the implemented AI solution <, all other files used for your experiments, and the walkthrough video should also be labeled similarly to your student ID.

Make sure your student ID and name are on all documents # your submission.

Logis6cs

After the ICA briefing has been given, you will be provided with opportunities to progress your in-course work during some <metabled sessions. Feedback – but no points – will be given on your work in progress to assist you in submitting a considered and well-developed ICA submission.

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Academic Misconduct and Plagiarism

Please note that the University takes the issue of academic misconduct and plagiarism very seriously. You should not copy anyone else’s work or use copyright materials without acknowledgment.