This is the illustrative report from the Esmond homepage, on a fictional master's thesis about news avoidance among young adults. The thesis, the data and the student don't exist. In a real review, the feedback is AI-generated and can contain errors. Each part below appears as it does in this sample, with a note on what it is for; headings in your own report can differ.
The cover says which document was reviewed and how many comments to expect. The counts show how much there is to read. Esmond gives no grade and no score.
Illustrative sample report
Master's thesis review
News avoidance among young adults: a quantitative study of motives and media use
Master's thesis
14
5
9
Fictional example. No real student, advisor or college is involved.
A short reading of the whole paper: what works, where it falls short and what the next draft could aim for. It comes first because it shapes how you read the rest.
Illustrative sample report
Verdict
Your research question is sharp and your data collection is carefully done. Where the thesis stalls is the step from description to interpretation: your results chapter describes what you found, but your discussion does little with it. As a result, your conclusion reads as a summary rather than an answer. You can fix this in your next draft.
Your execution holds up. Your discussion can be more ambitious: a reader who already knows your results learns nothing new in your final chapter. Make one claim that only your data can support, and build the discussion around it.
Fictional example. No real student, advisor or college is involved.
The issues that matter most, in the order to address them. Each one says what to look at and why. The revising stays with you.
Illustrative sample report
Your conclusion currently repeats the findings of chapter 5. Answer each sub-question in one sentence, and use the rest to interpret.
In five places you write “effect” and “leads to,” but a cross-sectional survey measures association. Replace that wording or explain why you keep it.
You discuss three models in chapter 2, but your questionnaire is based on one of them. Explain why, or cut the two you do not use.
Fictional example. No real student, advisor or college is involved.
You get your own PDF back with comments next to the passages they are about, each marked as important or as a suggestion. This excerpt comes from the results chapter of the fictional thesis. Numbers in the labels follow the order of the comments across the whole document.
Illustrative sample report
5. Results
5.2 News avoidance and platform use. Table 4 reports the correlations between frequency of social media use and the three dimensions of news avoidance. Respondents who reported spending more than two hours a day on TikTok or Instagram scored higher on intentional news avoidance (M = 3.41; SD = 0.82) than respondents with less than an hour of use (M = 2.76; SD = 0.91). The relationship was moderate and significant (r = 0.31; p < 0.01). This shows that intensive social media use has a negative effect on the news consumption of young adults. The pattern was consistent across the three age groups, although the relationship was strongest among 18- to 21-year-olds. For the dimension of unintentional news avoidance no significant relationship was found (r = 0.08; p = 0.21), suggesting the effect is mainly a matter of choice rather than exposure.
As for motives, respondents to the open-ended question mostly said that news gives them a sense of powerlessness (38 percent of answers), followed by a lack of trust in news media (24 percent) and lack of time (19 percent). Previous research confirms this picture. Notably, lack of time was hardly mentioned by heavy social media users, while it was the motive light users cited most often.
5.3 Differences by level of education. No significant differences were found between undergraduate and graduate students on the overall news avoidance scale (t(214) = 1.12; p = 0.26). Graduate students did mention lack of time more often as a motive, possibly related to the larger share of working students in that group.
5.4 Summary of results. In sum, intensive social media use is associated with more intentional news avoidance, while unintentional avoidance is unrelated to it. Powerlessness is the dominant motive, followed by distrust and lack of time. Level of education plays hardly any role. The next chapter sets these findings against the theoretical framework and discusses the limitations of the study.
Causal claim on cross-sectional data. You measure association, not effect.
“Previous research” without a source. Name at least one study.
The test (p = 0.26) covers the overall scale, not lack of time. Test that difference separately, or report it as a descriptive observation.
Here you correctly write “associated with”; earlier in this chapter you wrote “effect.” Choose one and use it throughout.
Fictional example. No real student, advisor or college is involved.
A full comment names what it found, explains why that matters and ends with a question or a step for you. It says what to change and why, and leaves the wording to you. This one belongs to comment 1 above.
Illustrative sample report
A causal claim based on data collected at a single point in time.
A survey at one point in time measures association. With r = 0.31 you cannot tell what is cause and what is effect: perhaps people who already avoid news simply spend more time on social media.
Can a measurement at one point in time show what is cause and what is effect? If not: which wording fits what your data actually show? And if you keep “effect,” how do you justify that in your method section?
Fictional example. No real student, advisor or college is involved.
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