Text 1Results from Dr. Martinez's team show that implementing AI-powered tutoring systems in high schools could dramatically improve student outcomes....
GMAT Craft and Structure : (Structure) Questions
Results from Dr. Martinez's team show that implementing AI-powered tutoring systems in high schools could dramatically improve student outcomes. Their pilot program data reveals that personalized learning algorithms increased test scores by 40% while reducing achievement gaps between different student populations. The technology adapts to each student's pace and learning style, creating an educational experience that's both efficient and effective—a breakthrough that could transform public education.
Text 2
Implementation of AI tutoring systems faces significant practical hurdles, despite Dr. Martinez's team's encouraging results. Extensive teacher training and substantial infrastructure investments would be necessary for successful deployment, as the researchers acknowledge. Schools must also develop comprehensive policies addressing student privacy and ensuring equitable access across different socioeconomic communities before widespread adoption.
Which choice best describes a difference in how the author of Text 1 and the author of Text 2 view Dr. Martinez's team's research?
The author of Text 2 questions whether the 40% improvement in test scores reported by Dr. Martinez's team is statistically significant, whereas the author of Text 1 accepts the findings.
The author of Text 2 approaches the research results with some reservation, whereas the author of Text 1 is enthusiastic about the reported educational benefits.
The author of Text 2 believes the AI tutoring systems are ready for immediate widespread implementation, whereas the author of Text 1 argues for more pilot testing.
The author of Text 2 focuses on the technology's effect on achievement gaps, whereas the author of Text 1 focuses on overall test score improvements.
Step 1: Decode and Map the Passage
Part A: Passage Analysis Table
Text 1:
| Text from Passage | Analysis |
|---|---|
| 'Results from Dr. Martinez's team show that implementing AI-powered tutoring systems in high schools could dramatically improve student outcomes.' |
|
| 'Their pilot program data reveals that personalized learning algorithms increased test scores by 40% while reducing achievement gaps between different student populations.' |
|
| 'The technology adapts to each student's pace and learning style, creating an educational experience that's both efficient and effective—a breakthrough that could transform public education.' |
|
Text 2:
| Text from Passage | Analysis |
|---|---|
| 'Implementation of AI tutoring systems faces significant practical hurdles, despite Dr. Martinez's team's encouraging results.' |
|
| 'Extensive teacher training and substantial infrastructure investments would be necessary for successful deployment, as the researchers acknowledge.' |
|
| 'Schools must also develop comprehensive policies addressing student privacy and ensuring equitable access across different socioeconomic communities before widespread adoption.' |
|
Part B: Passage Architecture & Core Elements
Main Point: Text 1 presents Dr. Martinez's AI tutoring research as a breakthrough with transformative potential, while Text 2 acknowledges the promising results but emphasizes the significant practical challenges that must be addressed before implementation.
Argument Flow: Text 1 builds enthusiasm by presenting strong research findings, providing compelling evidence, and concluding with transformative potential. Text 2 takes a more measured approach, acknowledging the positive results upfront but then systematically outlining the practical hurdles that complicate real-world implementation.
Step 2: Interpret the Question Precisely
This is a fill-in-the-blank question asking us to choose the best logical connector. The answer must create the right relationship between what comes before and after the blank.
Step 3: Prethink the Answer
- Text 1 takes an enthusiastic, optimistic approach - using words like 'dramatically,' 'breakthrough,' and 'transform' without dwelling on potential problems
- Text 2, while acknowledging the results are 'encouraging,' immediately shifts focus to practical challenges and obstacles that need to be overcome
- The key difference is in tone and focus: Text 1 celebrates the potential, while Text 2 tempers excitement with practical realism about implementation challenges
The author of Text 2 questions whether the 40% improvement in test scores reported by Dr. Martinez's team is statistically significant, whereas the author of Text 1 accepts the findings.
- Text 2 never questions the statistical significance of the 40% improvement
- Text 2 actually calls the results 'encouraging,' showing acceptance of the findings
- Both authors seem to accept the research validity - they just have different perspectives on what it means
The author of Text 2 approaches the research results with some reservation, whereas the author of Text 1 is enthusiastic about the reported educational benefits.
- Perfectly captures the attitudinal difference we identified
- Text 1 uses enthusiastic language like 'dramatically improve,' 'breakthrough,' 'transform'
- Text 2 takes a reserved approach, acknowledging results as 'encouraging' but immediately focusing on 'significant practical hurdles'
- This matches our prethinking about enthusiastic versus cautious perspectives
The author of Text 2 believes the AI tutoring systems are ready for immediate widespread implementation, whereas the author of Text 1 argues for more pilot testing.
- This reverses the actual positions
- Text 2 clearly emphasizes implementation challenges, not readiness for immediate adoption
- Text 1 doesn't argue for more pilot testing - it presents the results as ready for transformative impact
The author of Text 2 focuses on the technology's effect on achievement gaps, whereas the author of Text 1 focuses on overall test score improvements.
- Both texts actually mention both test scores and achievement gaps
- Text 1 mentions both 'increased test scores by 40%' and 'reducing achievement gaps'
- The difference isn't about which metrics they focus on, but about their overall attitude toward the research