Exploring the impact of artificial intelligence application in personalized learning environments: thematic analysis of undergraduates’ perceptions in China

Wang, Xu, Zhang, Hao, Jie · Humanities and Social Sciences Communications · 2024 · education · 10.1057/s41599-024-04168-x · record wang2024-exploring-the-impact-of-artificial
tag layer · index · concepts · authors · עברית
Where this record's text came from. faceted 2026-08 by reading the locally held text (pdftext); every evidence string verified as an exact substring of that text before merging. method is coded observational-study because the vocabulary offers no qualitative-interview or thematic-analysis option; the paper's own description is "Thematic analysis was employed to organize and interpret the data." and "Through a qualitative examination involving 48 undergraduate students, the findings reveal that generative AI significantly enhances personalized learning and the development of cognitive skills, including creativity, critical thinking, and problem-solving." claim type is existence: the contribution is documenting that these perceived effects and concerns are present among students, not establishing a causal mechanism. Replication status is 'replicated' only in the weak sense that the paper repeatedly states its themes agree with named prior work; no direct replication of a specific prior study was attempted. Note the abstract says the study 'surveyed' 48 undergraduates while the Data availability section describes a 10-question survey on a PLE forum and the Methods describe ~30-minute interviews - the paper is internally inconsistent about the instrument, and I have quoted it rather than resolved it. 2026-08-14 and 2026-08-15 schema-0.3 passes: every quote verified as an exact substring of the staged text and anchored at merge time; concept terms verified present; every edge endpoint an existing node.
How to read this page. The article itself is not reproduced here — this paper is not cleared for redistribution, so what follows is the tag layer alone. The evidence sentences are withheld with the article. The table at the foot lists every tag the record carries; each row names the facet it belongs to, and the whose words column says whether the tag records the paper's own wording or the tagger's sentence about the paper. Every tag links to each other paper in the corpus carrying it. The machine-readable edition of the same thing is the TEI file beside this one, where the tags live in standOff, and the article is at its publisher via the link below. When we have changed our mind. Some entries in the table begin with a word in capitals and the reason for it. EVIDENCE CORRECTED — the finding still stands, but we had quoted the wrong sentence from the paper, and the quotation has been replaced with the right one. RETRACTED — the entry itself was wrong; it stays on the page, marked, with the reason, rather than being deleted. WITHDRAWN AND REPLACED — the entry was wrong, and a corrected version of it appears in the entries below. Each mark carries its own reason, in brackets, because the reasons differ from one entry to the next. An entry we have WITHDRAWN is removed from this page and kept in the record, with its reason. A line reading Audit and a date is a later re-reading of the entry against its whole sentence: DOUBTFUL means it was kept, and the reason says what is loose about it. Quotation marks inside the tagger's sentence. Where a tag is the tagger's own sentence, a phrase in quotation marks inside it is borrowed from the paper — the tagger's wording around it is not. The paper's own sentence is the evidence quote for that row; on this page it is withheld with the article, and the article is at its publisher. Nothing inside quotation marks anywhere on this page is ours: if it is quoted, it is the paper's. Whose words a concept carries. A concept entry records a term the paper itself uses, so the term, the sense given for it, and its evidence are the paper's own words, quoted where the paper fixes them. This is the opposite of a sense entry in the tag table, which is the tagger's sentence about how the paper uses a term. Where a concept entry could not be grounded in the paper's words, the entry says so on its own row.
Other fields define these differently the same word, defined another way in another discipline
engagement 4 fields · thematic analysis 2 fields

Tags on this paper

Every tag the record carries, each linking to every other paper in the corpus that carries it. A tag appears here whether or not its words occur in the article — the spans below are only the ones a sentence in this paper licenses. Of the 7 concept tags on this paper, all 7 are grounded in a quoted sentence.
cognitive skills personalized learning generative AI semi-structured interview convenience sampling thematic analysis motivation AI supplies instant feedback and flags common mistakes, which triggers student self-reflection and thereby develops cognitive and thinking skills the same personalization and tutoring that improves the learning experience invites excessive reliance, which erodes learner autonomy and critical thinking personalized learning environment (PLE) personalized learning environment engagement learning engagement inter-rater agreement observational-study

Legend

relation to other workclaim_relationshow this paper stands to another: agrees, contradicts, extends, qualifies
claim typeclaim_typethe shape of the claim: existence, mechanism, method, critique, prediction, null
concept edgeconcept_edgesan asserted relation between two concepts, with the sentence asserting it
conceptconceptsan idea the paper works with, quoted where the paper uses it
content tagcontent_tagsa finding, definition, quantity, statistic or variable named in the paper
controlcontrolsan experiment run to eliminate a rival explanation
limitationlimitationswhat the paper says its evidence cannot support
mechanismmechanismshow the reported effect is said to come about
methodmethodhow the paper produced its evidence
phenomenonphenomenonthe effect or regularity under study
sensesensesa distinct use of a term, quoted where the paper fixes it

The paper

The full text is not reproduced here. This article is not cleared for redistribution, so the page carries the tag layer only. Its evidence sentences are withheld; the grounding status remains with each tag, and the article itself is at https://doi.org/10.1057/s41599-024-04168-x. The reading view exists for the papers whose licence permits it.

Every tag on this page

claim_relations 4 · claim_type 1 · concept_edges 1 · concepts 7 · content_tags 22 · controls 1 · limitations 8 · mechanisms 2 · method 1 · phenomenon 1 · senses 4

Relations on this page carry a quotation for the claim. Where a relation was separately evidenced — and so tested against the vocabulary for its type — the row says so; none of 1 relation here carry that mark. The rest are the tagger’s reading of the sentence quoted beside them.

facettagfieldconf.whose words
claim_typeexistence
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claim_typethe paper's words
phenomenonundergraduates' perceptions of generative AI in personalized learning
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phenomenonthe paper's words
methodobservational-study
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methodthe paper's words
mechanismsmechanisms
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mechanisms[0]the tagger's words
mechanismsmechanisms
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mechanisms[1]the tagger's words
limitationsSmall sample and purely qualitative data limit generalizability and may carry subjective bias.
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what this limits: the generalisability of every finding: THE LIMITED SAMPLE SIZE ... AND THE EXCLUSIVE RELIANCE ON QUALITATIVE DATA
limitations[0]the tagger's words
limitationsNo long-term or longitudinal evidence about sustained effects of AI use on learning, cognition or motivation.
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what this limits: any claim about sustained effects: THE STUDY DOES NOT ADDRESS THE LONG-TERM EFFECTS OF AI USAGE IN EDUCATION
limitations[1]the tagger's words
limitationsThree limits are stated together: a small sample, exclusive reliance on qualitative data with subjective bias, and no long-term effects.
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what this limits: these findings as evidence about AI in personalised learning generally: the authors name a limited sample, exclusive reliance on qualitative data which may introduce subjective biases, and no treatment of long-term effects
limitations[2]1.0the tagger's words
limitationsThe central construct is measured through instruments derived from a taxonomy, so the result inherits that taxonomy's division of cognition.
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what this limits: a claim about cognitive skills in this study: they are measured with tools developed under guidance from Bloom's Taxonomy, which fixes which abilities count
limitations[3]1.0the tagger's words
limitationsThe paper describes its own data collection two incompatible ways. Methods: interviews of about 30 minutes, audio-recorded and transcribed verbatim. Data availability: a survey of 10 questions answered as real-time feedback posted directly on a forum. These are different instruments producing different data, and the abstract uses both words in one sentence ('surveyed 48 undergraduates ... through semi-structured interviews'). Nothing in the paper reconciles them.
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limitations[4]0.9the tagger's words
limitationsThe 90% agreement is computed after the two coders built the themes together: the preliminary codes were 'collaboratively discussed and categorized into potential themes by both the first and second authors', and only then did each researcher independently apply them. The figure measures consistency of application, not independent arrival at the same scheme.
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limitations[5]0.9the tagger's words
limitationsThe sample is selected on the exposure under study - students who chose to enrol in an 'AI in Academic Writing' course - and the paper offers that selection as what makes the data informative rather than as a threat to it. Perceptions of AI are then reported from a group defined by having opted into using AI.
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limitations[6]0.95the tagger's words
limitationsThe paper recommends that future work 'employ quantitative methods alongside qualitative approaches to validate observed trends', while its own results are reported almost entirely as counts out of 48 - 35, 29, 25, 24, 22, 21, 18, 15, 10, 9 students and so on. The trends it asks future work to validate quantitatively are already stated as numbers here.
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limitations[7]0.9the tagger's words
controlsA PROCEDURAL SAFEGUARD ON INSTRUMENT DESIGN: each interview question grounded in both a theoretical definition and actual student responses
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eliminates: construct drift between what the instrument asks and what it claims to measure - a question written only from theory can miss how students actually talk, and one written only from their words can measure something the construct does not name · outcome (stated by the tagger, no quotation): held
controls[0]0.85the tagger's words
conceptscognitive skills
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concepts[0]the paper's words
conceptspersonalized learning
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concepts[1]the paper's words
conceptsgenerative AI
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also in: generative AI
concepts[2]the paper's words
conceptssemi-structured interview
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concepts[3]0.95the paper's words
conceptsconvenience sampling
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concepts[4]0.95the paper's words
conceptsthematic analysis
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concepts[5]0.95the paper's words
conceptsmotivation
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also in: motivation
concepts[6]0.9the paper's words
sensescognitive skills - operationalised through Bloom's Taxonomy: memory, attention, comprehension and reasoning, as measured by tools built under that taxonomy's guidance
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senses[0]1.0the tagger's words
sensesAI assistance - AS EXPERIENCED BY A PARTICIPANT: a failure mode in which attempting to correct the system can INCREASE its misunderstanding rather than reduce it
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senses[1]1.0the tagger's words
sensesthe data - AS METHODS DESCRIBES IT: interviews of about 30 minutes, audio-recorded and transcribed verbatim by transcription software, then reviewed and edited
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senses[2]0.95the tagger's words
sensesthe data - AS DATA AVAILABILITY DESCRIBES IT: a survey of 10 questions on a platform, answered as real-time feedback posted directly on a forum. NOT THE SAME INSTRUMENT as the transcribed 30-minute interviews of the methods section
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senses[3]0.95the tagger's words
claim_relationsreplicates → Mao, Chen, and Liu (2024)
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claim_relations[0]the tagger's words
claim_relationsreplicates → Seo et al. (2021)
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claim_relations[1]the tagger's words
claim_relationsreplicates → Ghimire et al. (2024)
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claim_relations[2]the tagger's words
claim_relationsbuilds-on → Vasconcelos et al. (2023); Passi and Vorvoreanu (2022)
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claim_relations[3]the tagger's words
concept_edgescognitive skills —operationalised-as→ semi-structured interview
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strength of the relation: 0.9 · note: THE SAME SHAPE ON THE OTHER CONSTRUCT: '…generative AI can enhance your higher-order…' thinking and, if so, how AI achieves it. Both of the paper's two constructs are elicited by a question whose second clause presupposes the effect. The construct is not measured; AGREEMENT WITH A PROPOSITION ABOUT THE CONSTRUCT IS. (This span is column-interleaved in the staged PDF text with a neighbouring column so the quote is exact but the paper's line order is not recoverable from it.) Audit 2026-09-28: DOUBTFUL - read against its full span and kept, but the span is an interview question about cognitive skills; operationalised-as is loose.
concept_edges[1]0.9the tagger's words
content_tagsinter-rater agreement
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content_tags.statistics[0]the tagger's words
content_tagsThirty-five students said generative AI helped with homework and exam preparation by offering problem-solving strategies, refining articles and creating course content.
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content_tags.findings[0]the tagger's words
content_tagsForty-two students said AI supplied learning resources and suggestions tailored to their learning behaviour and past performance.
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content_tags.findings[1]the tagger's words
content_tagsThirty-three students credited generative AI with fostering their creativity by supplying diverse perspectives and innovative solutions.
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content_tags.findings[2]the tagger's words
content_tagsTwenty-two students worried that excessive reliance on AI could weaken their ability to think independently.
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content_tags.findings[3]the tagger's words
content_tagsTwenty-one students said AI-generated content was sometimes erroneous or inaccurate, so they had to verify and filter it themselves.
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content_tags.findings[4]the tagger's words
content_tagsTwenty-five students were worried about data security and privacy because of the volume of personal data AI systems process.
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content_tags.findings[5]the tagger's words
content_tagsFifteen students said the AI tools failed to fully understand their queries and returned answers below their expectations.
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content_tags.findings[6]the tagger's words
content_tagspersonalized learning environment (PLE)
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content_tags.definitions[0]the tagger's words
content_tagspersonalized learning environment
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content_tags.definitions[1]the tagger's words
content_tagsengagement
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also in: engagement
content_tags.definitions[2]the tagger's words
content_tagsmotivation
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also in: motivation
content_tags.definitions[3]the tagger's words
content_tagscognitive skills
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content_tags.variables[1]the tagger's words
content_tagsmotivation
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also in: motivation
content_tags.variables[2]the tagger's words
content_tagslearning engagement
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content_tags.variables[3]the tagger's words
content_tags48 — number of undergraduate interviewees in the study
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content_tags.quantities[0]the tagger's words
content_tags23 males and 25 females, aged between 18 and 21 — sex and age composition of the student cohort
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content_tags.quantities[1]the tagger's words
content_tagsover 90% — agreement between the two coders on themes, codes and quotations
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content_tags.quantities[2]the tagger's words
content_tagsfour hours weekly — minimum required AI-platform use per student per week
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content_tags.quantities[3]the tagger's words
content_tags35 — students reporting AI helped with homework and exam preparation
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content_tags.quantities[4]the tagger's words
content_tags42 — students reporting AI-customized resources boosted their enthusiasm
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content_tags.quantities[5]the tagger's words
content_tags22 — students fearing over-reliance would reduce independent thinking
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content_tags.quantities[6]the tagger's words

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