Success Stories

A final-year undergraduate on an Applied Linguistics and TESOL programme came to MAAS carrying two demanding modules in one semester: a language-teaching methodology module (UPNQ9L-30-3), and a psycholinguistics module, Language(s) in the Mind (UPNQ9Q-30-3), on how language is represented and processed in the mind. The second was assessed by an oral viva, where the questions are released in advance and examiners then probe understanding live. For an international student working in English, defending psycholinguistic theory aloud is a different order of difficulty from writing it.

We matched the student with a discipline-matched tutor and ran a sixteen-session package per module. This is tutoring, not ghost-writing: the student attended, prepared and sat every assessment themselves. In the methodology module the tutor worked through the second-language-acquisition frameworks the brief expected, then had the student apply them to their own examples. For the viva module the tutor took the released questions one by one, unpacked the underlying models (lexical access, speech production and comprehension, the bilingual mental lexicon) and ran mock viva questioning, so the student could explain each framework in their own words and field follow-ups without freezing.

The student did the thinking and the talking. The tutor made the frameworks clear and the viva rehearsable, so on the day the student carried their own understanding into the room.

A Master's student in Supply Chain Management took on a dissertation that integrates an AI agent (an automation workflow driving a large language model) into supply-chain logistics. The catch was the scope: the work had to meet the engineering department's standards, yet the student came from a business background with no technical grounding. They needed a mentor who could hold both sides, the supply-chain theory and the build, without ever taking the keyboard.

We matched the student with a mentor who works as a software architect and AI automation specialist. In the first phase, against a tight research-proposal deadline, the mentor reviewed the draft, found the logic errors and helped the student restructure the proposal into something an engineering panel would accept. In the ongoing phase the student self-builds the model while the mentor troubleshoots the algorithm alongside them and keeps pressing the conceptual link between supply-chain theory and the AI-agent method, so the dissertation argues a point rather than just demonstrating a tool.

The research proposal was completed and submitted on deadline. The student is now building and writing toward final submission, with the mentor reviewing the work along the way. The student writes every word; the mentor makes the hard parts tractable.

A Master's student on a Global Supply Chain and Operations Management programme faced a thesis with exacting conditions: roughly 70 to 80 pages, a mandatory original contribution rather than a literature summary, a strong methodological focus, and a research proposal (exposé) that had to follow the school's template and cite 50 to 60 academic sources before it would be approved.

This is mentoring, not writing: the student writes, we guide and review. After a free kick-off to align scope and schedule, the mentor ran a feasibility pass over the candidate topics and set a data strategy that favoured public datasets over proprietary company data, as the brief required. The mentor then helped the student formulate a formal research question and a methodological framework, and mapped the literature needed to reach the reference threshold. Before the supervisor meeting, the mentor reviewed the student’s own exposé drafts against the school’s standard.

The exposé is being finalised to the required standard ahead of the formal writing window. The student owns every draft; the mentor makes sure each phase clears the bar before the next begins.

A finance undergraduate at RMIT Vietnam had an individual report due that ran from CFA-level fixed-income mathematics through to a macro call on central-bank policy, on a tight turnaround. They needed someone who could make the quantitative machinery click before the deadline, not write the report for them.

We matched the student with a tutor who holds an MSc in Financial Engineering. In a focused one-to-one session the tutor worked through bond valuation and portfolio management, including calculating Macaulay duration and convexity in Excel, then moved to the macro side: forecasting the yield curve and reading the likely impact of a Bank of England rate decision. The tutor also showed the student how to pull data from an industry terminal. Throughout, the expert reviewed the student’s own draft, flagged the errors and explained the theory beneath them, so the student could finish the report themselves in RMIT Harvard style.

The student left the session with the calculations and the argument they needed, and completed and submitted their own report.

An undergraduate faced a Foundations of Business Analytics subject with a final exam worth half the grade and a hurdle: pass the exam or fail the subject. The course moved quickly across twelve weeks of R, and the student needed the whole syllabus consolidated and the exam format drilled before the day.

We ran a ten-session tutoring package in two phases. Phase one consolidated the theory across weeks one to twelve: descriptive, predictive and causal analytics; data visualisation with ggplot2; data wrangling with dplyr (pipes, joins, tidy-data pivots); and predictive modelling with OLS, lag-1 baselines and MAPE. Phase two was a full mock-exam walkthrough, covering the multiple-choice traps in part A and the short-answer writing in part B, where the student practised writing business interpretations to the marking rubric. The tutor demonstrated and guided; the student wrote and sat the exam themselves.

The student went into the final having covered the full syllabus and rehearsed the exam format under timed conditions.