Research note: built from the MSCAIML Programme Guide (School of Computer and Information Sciences, July 2026, version 1), the MSCAIML programme page on ignou.ac.in (checked 06 October 2026), the IGNOU Common Prospectus July 2026 and, as SOCIS precedent only, the MCSP-232 MCA project guidelines. IGNOU has not yet issued MCSP-090 project guidelines; this page will be updated when it does.
The IGNOU MSCAIML project is MCSP-090 Project Work: 12 credits in Semester IV, marked out of 200 of the programme’s 1,900 marks. As of 06 October 2026, IGNOU has published only the course description in the MSCAIML Programme Guide (July 2026): eight learning outcomes and a list of topic areas. Separate MCSP-090 guidelines and a proforma are promised but not yet issued, so the route, dates and guide rules are still unknown. MCSP-079 (MSCDSA) and MCSP-232 (MCA) rules are not MSCAIML rules.
Facts most pages miss
- The MSCAIML project code is MCSP-090 (Programme Guide July 2026, p. 7). The Common Prospectus July 2026 prints the MSCDSA course list, ending in “MCSP 079”, under the MSCAIML heading on p. 80, and the correct MSCAIML list, ending in “MCSP 90”, on pp. 81 to 82.
- As of 06 October 2026 there are no MCSP-090 project guidelines or proforma. The guide says they “will be provided and made available on the university’s website” (p. 49), and the programme page links none.
- The project is 12 of 80 credits and 200 of 1,900 marks (guide pp. 7 and 49), twice the weight of a 4-credit theory course.
- Only 4 project counselling sessions, 8 hours in all, are scheduled in Semester IV (guide p. 12). Most of the work happens with your guide, not in class.
- MCSP-090 may be industry-based, research-oriented, application-based or a socially relevant AI solution, and its topic list names Generative AI, Explainable AI and AI ethics (guide p. 48). The MCA project bars theoretical projects and demands software development, so an MCA-style synopsis built around DFDs and ER diagrams misses what MCSP-090 asks for.
- The project evaluation fee for any project above 4 credits is Rs 500, paid with the term-end examination form (Common Prospectus July 2026, p. 27). MCSP-090 is 12 credits.
MCSP-090, Not MCSP-079 or MCSP-232: The IGNOU MSCAIML Project Code
The project course of the M.Sc. (Artificial Intelligence and Machine Learning), programme code MSCAIML, is MCSP-090 Project Work. It carries 12 credits and sits in Semester IV beside MCS-073 Soft Computing and MCS-089 Reinforcement Learning (MSCAIML Programme Guide, July 2026, p. 7). The same School of Computer and Information Sciences runs two other 12-credit Semester IV projects, and their codes are the ones students mix up.
Write the code exactly as the guide does, MCSP-090, on every document. The prospectus spelling “MCSP 90” and search spellings such as MCSP 090 or MCSP090 all refer to the same course. If a site, a forum post or a downloaded PDF talks about MCSP-079, it is describing the Data Science programme, whose project description in the January 2026 MSCDSA guide stresses software engineering practice for data processing, analysis and visualisation. For a lookup across all IGNOU programmes, see the IGNOU project course codes list.
Document Map: What IGNOU Has Published for MCSP-090 So Far
Three kinds of document matter, and they carry very different weight. Official MSCAIML documents state the rules. SOCIS precedent shows how the School has run a similar project. Study reference files show a format and are never submitted.
| Status | Document | Issuer and date | What it settles for MSCAIML |
|---|---|---|---|
| OFFICIAL | MSCAIML Programme Guide | SOCIS, July 2026, version 1 | Code, credits, semester, 200 marks, the eight project outcomes, topic areas, counselling sessions |
| OFFICIAL | MSCAIML programme page | IGNOU, live page, checked 06 Oct 2026 | Rs 13,000 per semester, 24 to 48 months, course list, coordinators |
| OFFICIAL | Common Prospectus | IGNOU, July 2026 | Rs 500 project evaluation fee (p. 27); January and July sessions (p. 34); the misplaced course table (p. 80) |
| OFFICIAL | MSCAIML Semester I assignments | SOCIS, July 2026 | The first batch started in the July 2026 session (assignments due 31 October 2026) |
| NOT YET ISSUED | MCSP-090 project guidelines and proforma | SOCIS, not found on 06 Oct 2026 | Route, submission dates, proforma, guide criteria, report length, viva weight |
| SOCIS PRECEDENT | MCSP-232 project guidelines (MCA_NEW) | SOCIS, July 2026 and January 2027 edition | How the School runs the MCA project. Not an MSCAIML rule |
| STUDY REFERENCE | Sample synopsis (PDF, 12 pages) | ignouproject.com, October 2026 | Section logic tied to the eight outcomes. Not for submission |
| STUDY REFERENCE | Report format template (PDF, 10 pages) | ignouproject.com, October 2026 | Page order and empty table shells only. Not for submission |
Reinforcement learning for adaptive traffic signal timing in a simulated junction. Every setting and result is left in [brackets].
Download sample synopsis
Nine chapters, each tied to a guide outcome, with empty results tables and sentence frames. No data.
Download report template
Question about your MSCAIML project? Message us on WhatsApp at +91 93546 37830. We answer from the official documents and say plainly when IGNOU has not decided something yet.
Chat on WhatsAppWhat the MSCAIML Guide Fixes Today, and What It Leaves Open
A student planning MCSP-090 in October 2026 can rely on some facts, can only infer others, and must wait for the rest. Mixing these up is how wrong advice spreads before a new programme’s rules arrive.
| Question | Answer as of 06 October 2026 | Status |
|---|---|---|
| Course code, credits, semester | MCSP-090, 12 credits, Semester IV | OFFICIAL Guide p. 7 |
| Marks | Project computed out of 200; degree total 1,900; division bands from 40 per cent (third) to 75 per cent and above (first with distinction) | OFFICIAL Guide p. 49 |
| What the project must show | The eight outcomes, from problem definition to viva defence | OFFICIAL Guide p. 48 |
| Allowed project types | Industry-based, research-oriented, application-based, or socially relevant AI solution development; “preferably” in a software industry or research organisation | OFFICIAL Guide pp. 48 to 49 |
| Counselling | 4 project sessions of 2 hours each in Semester IV | OFFICIAL Guide p. 12 |
| Fee | Rs 500 project evaluation fee, with the term-end examination form | OFFICIAL Prospectus p. 27 |
| Where the synopsis goes, and when | Not published. In the MCA project, the Regional Director of your Regional Centre, in two slots a year | PRECEDENT |
| Who can be the guide | Not published. MCA criteria are summarised on the guide eligibility page | PRECEDENT |
| Proforma, report length, spacing, split of the 200 marks | Not published | PENDING |
When the First Batch Reaches the Project
The programme runs two semesters a year, July to December and January to June, with admissions in both cycles (Prospectus July 2026, p. 34). Counting forward from the first semester gives the earliest Semester IV for each batch. These dates follow from the semester calendar in the guide; the proposal and report dates for MCSP-090 are not yet published.
| Admission cycle | Semester I | Semester II | Semester III | Semester IV (MCSP-090) | Term-end exam for Semester IV courses |
|---|---|---|---|---|---|
| July 2026 (first batch) | July to December 2026 | January to June 2027 | July to December 2027 | January to June 2028 | June 2028 |
| January 2027 | January to June 2027 | July to December 2027 | January to June 2028 | July to December 2028 | December 2028 |
The MCA project lets students send a proposal once they enter Semester III. Whether MCSP-090 will do the same is not stated. Use Semester III to settle your topic, guide and data source, so that you are ready whichever rule IGNOU adopts. The maximum duration is 4 years (48 months) from admission.
IGNOU MSCAIML Project Topics Your Own Courses Prepare You For
The guide lists six topic areas and says students “may select topics from (but not limited to)” them. It also asks for topics “sufficiently complex and substantial to qualify as a M.Sc. Artificial Intelligence and Machine Learning project”. The third column below shows which MSCAIML courses already teach the methods, so your synopsis can point to them. The last column is planning advice, not an IGNOU rule.
| Topic area in the guide | Examples the guide names | MSCAIML courses that prepare you | PLANNING-AID Watch before you choose |
|---|---|---|---|
| Machine Learning | Regression, classification; clustering, dimensionality reduction; reinforcement learning | MCS-084 Blocks 2 to 4; MCS-085; MCSL-086; MCS-089 | You need a labelled dataset you are allowed to use; check class balance early |
| Deep Learning | CNN for image classification; RNN or LSTM for time series; Transformer-based NLP | MCS-072 Units 5 to 7 and 10; MCSL-076 | Training time and GPU access; transfer learning (Unit 10) often makes a project feasible |
| Natural Language Processing | Sentiment analysis, chatbot development, text classification | MCS-077 Units 6, 13 and 15 | Chat or review data may contain personal details; Indian-language data is scarcer |
| Computer Vision | Object detection, face recognition, medical image analysis | MCS-230 Units 11 to 13; MCSL-088 | Faces and medical images are personal data; record consent and anonymisation |
| Data Science and Analytics | Predictive analytics, recommendation systems, fraud detection | MCS-084; MCS-066; MCS-207 | Real fraud and customer data is usually confidential; use a public or permitted source |
| Emerging areas | Generative AI, Explainable AI, AI for healthcare, agriculture and education, AI ethics and responsible AI | MCS-077 Unit 6; MCS-072 Units 9 and 12 | Outcome 5 still applies: decide how generated or explained output will be measured |
Reinforcement learning (MCS-089) and pattern recognition (MCS-085) are taught in MSCAIML and not in the MSCDSA programme, so a topic built on them fits this degree well. A simulation-based reinforcement learning project also avoids personal data altogether, which is why the sample synopsis uses one.
Eight Outcomes, Eight Synopsis Sections
Until IGNOU issues an MCSP-090 synopsis format, the eight outcomes in the guide are the only official yardstick. A proposal that shows each of them answers the questions an evaluator is most likely to ask. The mapping below is how the sample synopsis is organised.
| Outcome in the guide (p. 48) | Synopsis section that shows it | What the section must contain |
|---|---|---|
| 1. Identify and define a real-world AI/ML problem | Title, introduction, problem statement, objectives | The problem in one sentence, the ML task type, neutral objectives (no assumed result) |
| 2. Conduct a structured literature survey | Literature survey | Sources grouped by theme, each with method and metric, then the gap |
| 3. Collect, preprocess and analyse datasets | Data or environment | Source, licence or permission, size, features, target, preprocessing and exploratory plan |
| 4. Apply suitable ML or DL algorithms | Methodology | Each model and a baseline, with the reason for choosing it |
| 5. Evaluate model performance using appropriate metrics | Evaluation plan | Metrics that match the task, validation scheme, how test data stays unseen |
| 6. Interpret results and suggest improvements | Interpretation plan, limitations, ethics | How results will be read, what could go wrong, data and fairness safeguards |
| 7. Prepare a professional technical report | Work plan, tools, proposed chapters | Timeline, software versions, reproducibility, report outline |
| 8. Present and defend the project in the viva voce | Viva readiness | What you will demonstrate and explain |
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OFFICIAL-RULE MCSP-090 Eight-Outcome Coverage Check
Tick what your draft synopsis already shows. The check uses only the eight project outcomes and the topic list in the MSCAIML Programme Guide (July 2026, pp. 48 to 49). It does not predict approval.
Have Your MCSP-090 Synopsis Checked Before It Goes In
A first-batch student has no senior’s approved synopsis to compare against and no MCSP-090 format to follow. The most common weak point in an ML proposal is not the model: it is a metric that does not fit the task, a dataset with no stated source or permission, or objectives that already assume the result. We check your own draft against the eight outcomes in the guide and, where IGNOU has not yet decided, against how SOCIS runs its MCA project, and return it with every correction marked.
What we check in your synopsis
- Each of the eight guide outcomes is visibly covered
- Objectives are neutral and testable, with no assumed finding
- Dataset source, licence or permission, size and target are stated
- Metrics match the task, and a baseline is named
- Validation keeps test data unseen until the end
- Personal data, consent and bias are addressed
- Title, guide and form details are complete and consistent, the point on which SOCIS summarily rejects MCA proposals
What we check in your report
- Every number traces to your own runs and can be reproduced from stated versions and seeds
- Results use the metrics promised in the approved synopsis
- Interpretation, error analysis and limitations are present
- The title matches the approved title word for word
- Front matter and signatures are in the order IGNOU asks for
- Tables, figures and pages are numbered and listed
Synopsis correction Rs 1,000. Project report correction Rs 2,000.
See how synopsis and report correction worksYou remain the author of your synopsis and report, and IGNOU’s evaluator alone decides approval. We correct your draft; we do not write it, supply data or results, or sign any form.
Precedent Proforma, Box by Box
PRECEDENT FIELD MAP IGNOU has not issued an MCSP-090 proforma. The map below is the SOCIS “Proforma for the Approval of MCA Project Proposal (MCSP-232)”, July 2026 and January 2027 edition, p. 5. It shows what the School has asked for on its nearest form. Fill only the form IGNOU issues for MCSP-090, and replace this map when it does.
| Box on the MCSP-232 form | Who fills it | What to watch for an AI/ML proposal |
|---|---|---|
| Enrolment No. | Student | Must match your ID card; quote it in every letter |
| Study Centre Code and Study Centre Name | Student | The MSCAIML guide calls these Learner Support Centres |
| Regional Centre and RC Code | Student | In the MCA project the proposal goes to this centre’s Regional Director, never to SOCIS |
| E-mail and Mobile No. | Student | Contacts you will still use months later |
| 1. Name and address of the student | Student | Same spelling as the enrolment record |
| 2. Title of the project | Student with guide | Kept word for word through the report; name the task, the data or environment, and the method family |
| 3. Name and address of the guide | Guide | Working address |
| 4. Educational qualification of the guide (Ph.D, M.Tech, B.E./B.Tech, MCA, M.Sc; starred degrees in Computer Science or IT only), bio-data attached | Guide | MCA criteria: Ph.D or M.Tech in CS with 1 year’s experience, or B.E./B.Tech (CS), MCA or M.Sc (CS) with 2 years. MCSP-090 criteria not yet issued |
| 5. Working or teaching experience of the guide | Guide | MCA rule: no more than 5 IGNOU MCA students per guide at a time |
| 6. Software used in the project | Student with guide | For ML: Python version, libraries (MCS-081 Unit 11 names NumPy, Pandas, Matplotlib, scikit-learn, PyTorch, TensorFlow) and compute |
| 7. Is this your first submission? Yes or No | Student | A resubmission encloses the earlier non-approved proforma |
| Signature and date of the student; signature and date of the guide | Both | In MCA the guide’s signature must later match the Certificate of Originality |
| Office use: name, signature, designation and stamp of the Project Proposal Evaluator; Approved or Not Approved; date | Evaluator | The evaluator decides, not the guide |
| Suggestions for reformulating the project | Evaluator | Must be carried into the revised proposal or, if approved, into the final report |
| Project Proposal No. | Office | Quote it with your enrolment number in all correspondence |
Guide bio-data checklist (MCA precedent)
- Bio-data signed and dated by the guide, with a certified copy of the guide’s identity proof.
- Qualification and discipline match the box ticked on the form.
- Years of working or teaching experience stated.
- Number of IGNOU students the guide currently supervises.
- The same signature you will later need on the Certificate of Originality.
Pick the Metric Before You Train the Model
The fifth outcome in the guide is to “evaluate model performance using appropriate metrics”. The metric decides what data you need, how you split it and what your results chapter will look like, so it belongs in the synopsis, not after the experiments. Choose the task type below to see a standard metric set and where your own MSCAIML courses teach it.
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PLANNING-AID MSCAIML Task-to-Metric and Course Matcher
IGNOU does not prescribe metrics for MCSP-090. This aid suggests standard metrics for each task type and points to the MSCAIML course units (Programme Guide, July 2026) that teach them. Agree the final choice with your guide.
Report Format Template for an ML Project
IGNOU has not set a page count, spacing or chapter list for the MCSP-090 report. The seventh outcome asks for “a professional technical report”, and the report has to carry evidence of all eight outcomes. The chapter order below, used in the free report template, puts each outcome in one place an examiner can find quickly.
| Chapter | Outcome it evidences | Must contain |
|---|---|---|
| 1. Problem definition and objectives | 1 | Problem statement, task type, neutral objectives, scope |
| 2. Literature survey | 2 | Literature matrix (source, problem, method, data, metric, gap) |
| 3. Data or environment | 3 | Dataset description, permission, preprocessing log, exploratory analysis |
| 4. Methodology | 4 | Models, baseline, configuration, hyperparameter search |
| 5. Experimental setup | 4, 5 | Versions, hardware, seeds, validation scheme |
| 6. Results and evaluation | 5 | Test-set results for every model, repeated-run spread, significance where tested |
| 7. Interpretation, limitations, improvements | 6 | Error analysis, threats to validity, improvements tried or proposed |
| 8. Ethics and responsible AI | 6 | Privacy, consent, bias checks, limits on real-world use |
| 9. Conclusion and future scope | 1, 6 | Each objective answered from the results |
| References and appendices | 2, 7 | One reference style; code listing, repository link, environment file |
Results in your report must come from your own data and your own runs. The template leaves every results table empty for that reason. A number taken from a sample or another report is invented data, and the University treats copied work as unfair means. For the general sequence of project steps across IGNOU programmes, see the project submission guide, and for status checks after you submit, the project status guide.
What the MCA Project Rules Suggest, and Where They Will Not Fit
The MCSP-232 guidelines show how SOCIS has run a 12-credit Semester IV project. Some of their rules may carry over to MCSP-090; at least one clearly cannot. They also contradict themselves twice, which is worth knowing before anyone quotes them to you as settled.
| MCSP-232 rule | What it says | Fit for an MSCAIML project |
|---|---|---|
| Route | Proposal and report go to the Regional Director of your Regional Centre; the proposal is never sent to SOCIS | Plausible, unconfirmed |
| Proposal slots | 1 April to 30 June or 1 October to 31 December | Unconfirmed |
| Approval time | Printed as 30 days (p. 4), four to six weeks (p. 9) and 6 to 8 weeks (p. 11) in the same document | Plan for the longest |
| Synopsis length | Printed as 14 to 18 pages (p. 6) and 15 to 20 pages (p. 10) | No MCSP-090 length set |
| Guide | Ph.D or M.Tech in CS with 1 year, or B.E./B.Tech (CS), MCA or M.Sc (CS) with 2 years; at most 5 IGNOU MCA students | Unconfirmed; an AI/ML guide may need different criteria |
| Project type | Software development compulsory; theoretical projects and system studies not allowed | Does not fit: MCSP-090 allows research-oriented projects |
| Report | About 100 to 125 pages excluding code, single line spacing, CD of executables | Unconfirmed |
| Marks | Report 150 plus viva 50, at least 40 per cent in each | The 200 total matches MCSP-090; the split is not published |
| Viva | Face to face at the Regional Centre; demo and live coding may be asked | Outcome 8 makes a viva certain; its format is not published |
The full MCA rules, with both delivery routes, are explained on the MCA MCSP-232 project page.
MSCAIML vs MSCDSA Project: Same School, Different Course
| MSCAIML | MSCDSA | |
|---|---|---|
| Project course | MCSP-090 Project Work | MCSP-079 Project Work |
| Credits and semester | 12, Semester IV | 12, Semester IV |
| Project emphasis in the guide | Apply AI and ML to real-world problems; industry, research, application or socially relevant work; eight stated outcomes and six topic areas | Software engineering practices within data science; building dependable software for data processing, analysis and visualisation |
| Courses unique to the programme that feed the project | MCS-081, MCS-084, MCS-085, MCS-089, MCS-230 | MCS-062, MCS-067, MCS-068, MCS-071, MCS-078 |
| Programme guide | July 2026, version 1 | January 2026, version 1 |
| Project guidelines | Not yet issued | Not yet issued |
Both programmes share the 200-mark project and the same coordinators, but the guides describe different projects. Do not borrow an MSCDSA sample, topic list or rule for an MSCAIML proposal.
MSCAIML Project Questions
What is the project course code for IGNOU MSCAIML?
MCSP-090 Project Work, 12 credits, Semester IV (MSCAIML Programme Guide, July 2026, p. 7). The prospectus also prints it as “MCSP 90”. MCSP-079 belongs to MSCDSA and MCSP-232 to MCA.
Are MCSP-090 project guidelines available?
Not as of 06 October 2026. The guide says the guidelines “will be provided and made available on the university’s website”. Until then, the eight outcomes and topic list in the guide are the only official MCSP-090 requirements.
How many marks does the MSCAIML project carry?
200, within a programme total of 1,900 (guide p. 49). How the 200 will be split between report and viva has not been published. You can test how the project affects your overall result with the IGNOU percentage calculator.
When can I start the MSCAIML project?
MCSP-090 is a Semester IV course. For the July 2026 batch that is January to June 2028. Whether a proposal can be sent during Semester III, as the MCA project allows, is not stated, so use Semester III to fix your topic, guide and data.
Is there a viva for MCSP-090?
The eighth outcome in the guide is to “present and defend the project during viva voce”, so a viva is part of the course. Its format, venue and weight are not yet published. Typical questions by chapter are collected on the project viva questions page.
What is the MSCAIML project fee?
Rs 500 as the project evaluation fee for projects above 4 credits, paid with the term-end examination form (Common Prospectus July 2026, p. 27). Check the exam form for the cycle in which you submit.
Can my guide approve my synopsis?
For MCSP-090 the approval process is not yet published. In the SOCIS MCA project, a Project Proposal Evaluator marks the proposal Approved or Not Approved; the guide signs but does not approve. For how proposals work across IGNOU programmes, see the proposal and synopsis guide.
Official Sources for This Page
- Programme Guide for M.Sc. (Artificial Intelligence and Machine Learning), SOCIS, July 2026, version 1: programme structure p. 7, counselling p. 12, MCSP-090 pp. 48 to 49, evaluation p. 49. Read 06 October 2026.
- MSCAIML programme page, IGNOU: fee, duration, courses, coordinators. Checked 06 October 2026.
- IGNOU Common Prospectus, July 2026: project fee p. 27, programme table p. 34, MSCAIML section pp. 80 to 82.
- MSCAIML Semester I assignments, July 2026, SOCIS.
- Programme Guide for M.Sc. (Data Science and Analytics), SOCIS, January 2026: contrast only.
- MCSP-232 Project Guidelines, MCA_NEW, SOCIS, July 2026 and January 2027 edition, compared with the July 2024 and January 2025 edition: precedent only.
For other programme project pages, start from the IGNOU projects hub.
ignouproject.com is an independent service and is not affiliated with or endorsed by IGNOU. Rules, fees and dates change: confirm with the official documents, your Regional Centre or mscaiml@ignou.ac.in before you act. Sample files on this page are study reference and must not be submitted.
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