Researcher studying how generative-AI reliance, evaluation literacy, and course policy shape student learning
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papers here
1
reviewed by Evolve
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first-author
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corresponding
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works · OpenAlex
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h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-09-21) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
Not yet scored: the snapshot indexes too few records to compute a reliable output measure. This axis would track publication volume and productivity over time; only one first-author paper (2026, arXiv) is currently in the catalog.
Impact—
Not yet scored: no citation or influence metrics are available in the provided data. This axis would measure citation counts, uptake of the 2026 reliance study, and real-world effect on course design once the record accumulates.
Focus100/100
Focus scores 100: every indexed paper in the snapshot falls in a single field, computing education / AI in higher education (field distribution: 1 paper). The reviewed work stays tightly on-topic, tying generative-AI reliance, evaluation literacy, and course policy together in one survey of 118 students. With only one indexed paper, this perfect concentration partly reflects a thin record rather than a long-term single-field program.
Collaboration32/100
Collaboration scores 32, based on 4 distinct coauthors in the snapshot. The recurring circle includes Mei Si, Sirajam Munira, Oshani Seneviratne, and Kristin Bennett, and the reviewed paper is first-authored without a corresponding-author flag. The moderate score reflects a compact, stable collaboration network rather than large multi-institution partnerships.
Activity—
Not yet scored: the data covers a single indexed year (2026), so research cadence cannot be measured. This axis would track publication regularity, submission venues, and sustained output across consecutive years once more records exist.
Reproducibility20/100
Reproducibility scores 20 (mean across 1 scored paper; Evolve rates its reproducibility readiness as low). The 2026 survey is cross-sectional with 118 students, and Evolve's review flags uncorrected regression tests; the record does not indicate public data or code. This score reflects a single low-readiness paper until further work reports materials sharing.
Research summary
Lydia Manikonda is profiled here from a thin bibliometric snapshot anchored by first-author work in computing education and AI in higher education. Her reviewed 2026 arXiv study, 'The Uneven Impact of Generative AI on Student Learning: Examining the Roles of Reliance, Evaluation Literacy, and Course Policy in AI-related Courses,' surveys 118 students and argues that the type of reliance students place on generative AI — not how often they use it — shapes the benefits they perceive, with evaluation literacy and course policy conditioning outcomes. Evolve's verdict calls the evidence suggestive but cross-sectional and notes uncorrected regression tests, matching a reproducibility score of 20 (mean across 1 paper; readiness rated low). The radar shows a highly concentrated record: focus scores 100 because all indexed output sits in a single field. Collaboration scores 32, with 4 distinct coauthors, including frequent collaborators Mei Si, Sirajam Munira, Oshani Seneviratne, and Kristin Bennett. Output, impact, and activity axes are not yet scored because the underlying data is insufficient. The open questions for this early-stage profile are whether the reliance typology replicates beyond one cross-sectional survey, whether course-policy effects generalize, and whether materials sharing improves reproducibility as the record grows.
Collaboration pattern
Snapshot data shows a compact network: 4 distinct coauthors, with Mei Si, Sirajam Munira, Oshani Seneviratne, and Kristin Bennett appearing as frequent collaborators. The reviewed 2026 paper is first-authored (no corresponding-author flag), consistent with a hands-on role in a small team. Collaboration scores 32 — modest, and likely to change as more of the record is indexed.
Research keywords
Highlights
Reliance type, not usage frequency, drives perceived GenAI benefits
Evaluation literacy and course policy as conditions of uneven AI impact
Timeline
- 2026Publishes first-author arXiv study 'The Uneven Impact of Generative AI on Student Learning: Examining the Roles of Reliance, Evaluation Literacy, and Course Policy in AI-related Courses' in computing education / AI in higher education.
- 2026The study reports a survey of 118 students, finding that reliance type — not usage frequency — shapes the perceived benefits of generative AI.
- 2026Evolve's review logs the paper as suggestive but cross-sectional, with uncorrected regression tests and low reproducibility readiness (score 20, based on 1 paper).
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Bibliometric scores come from a thin snapshot: one indexed paper, so focus (100) and reproducibility (20) rest on a single record.
- Output, impact, and activity axes are unscored — no citation counts, h-index, or multi-year publication history was provided.
- Institution, country, and ORCID are absent from the source data; the profile is name-resolved from public bibliographic data.
- Paper-level assessment relies on Evolve's own review verdict; no independent peer-review record is included.