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Cited by 99Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationEducational Technology in Learning

Mastering knowledge: the impact of generative AI on student learning outcomes

Open access
Pallant, Jessica L., Blijlevens, Janneke, Campbell, Alexander, Jopp, Ryan·2026·Studies in Higher Education
DOI 10.1080/03075079.2025.2487570
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Abstract

Generative AI (GenAI) has had a significant impact across industries since the launch of ChatGPT in late 2022. Much of the focus of existing research in the higher education space has considered the impact GenAI has had on academics and institutions. Conversely, research has been less focused on the impact this technology will have on students. Our research investigates how GenAI impacts student learning outcomes in higher education. We applied a quasi-experimental lens to analyse qualitative data of 192 student reflections and apply quantitative content analysis (QCA). Results indicate that a higher level of learning occurs when students use GenAI to construct and augment knowledge (mastery approach). In contrast, lower-level learning outcomes resulted from using GenAI procedurally without augmenting knowledge (procedural approach). Through a practical lens, the course curriculum can be designed to include GenAI to scaffold students’ learning from basic knowledge construction tasks to more complex augmentation of knowledge. Assessment design can be adjusted to promote mastery goal structures, encouraging students to critically engage with GenAI outputs rather than simply reproducing them, fostering optimal learning outcomes.

AI snapshot

Written from the paper’s full text.

TL;DR

This study uses 192 student reflections to examine how generative AI use relates to learning outcomes through mastery versus performance goal structures. It finds that constructive and augmentative GenAI use is associated with higher grades and higher-order learning capabilities, while procedural and regurgitative use is associated with lower outcomes.

Key findings

  • Students taking a constructive approach had significantly higher overall marks (76 vs 69.8, t(190) = −4.24, p < .01) and assignment marks (74.3 vs 68.8, t = −3.34, p < .01) than non-constructive users. Procedural use was associated with lower overall marks (69.8 vs 76, t = 4.25, p < .01) and assignment marks (68.8 vs 74.4, t = 3.35, p < .01).
  • Augmentative GenAI use was associated with higher overall marks (74.4 vs 71, t = −2.26, p < .05), but this was not reflected in assignment marks; regurgitative use was associated with lower overall marks (71 vs 74.6, t = 2.37, p < .05).
  • Logistic regressions showed augmenting knowledge positively affected applied knowledge (OR = 14.000, p < .01), learning autonomy (OR = 17.231, p < .01), and critical thinking (OR = 35.782, p < .001). Regurgitation negatively affected applied knowledge, learning autonomy, and critical thinking.
  • Constructing knowledge positively affected applied knowledge and critical thinking, with no significant effect for learning autonomy because all constructive users also showed autonomy. Procedural use negatively affected applied knowledge, learning autonomy, and critical thinking. Only 6 of 192 showed no discipline knowledge and 8 of 192 showed no information literacy, preventing analysis of general knowledge attainment differences.

Methods

  • Mixed-method quantitative content analysis (QCA) of 192 student reflections (approximately 75,000 words) from marketing units: two undergraduate units with 56 and 84 enrolments and one postgraduate unit with 58 enrolments; six students did not complete the assessment.
  • Students used GenAI to develop a definition of a unit concept in week 1, then after a 12-week semester wrote their own definition and compared/contrasted it with the AI-generated definition.
  • A theory-based coding framework captured mastery/performance goal structures, learning outcomes (information literacy, level of knowledge, discipline knowledge), and thinking capabilities (critical thinking, applied knowledge, learning autonomy). Two authors coded low/medium/high, checked by two more researchers using consensus-based intercoding; NVivo was used. Final unit marks and assignment marks were analysed with t-tests and logistic regressions. A pretest survey of 61 complete responses (32 undergraduate, 29 postgraduate) contextualized GenAI use.

Limitations

  • Stated: The study did not directly measure or manipulate goal structures due to ethical concerns; augmentative/constructive approaches were proxies for mastery goals, and regurgitative/procedural approaches were proxies for performance goals, limiting direct tests of goal structure adoption on learning.
  • Stated: The authors could not control for confounds, so cause-and-effect relationships cannot be tested; the work is presented as exploratory. They also note the study was conducted when ChatGPT-3 was the most commonly used model, and GenAI is rapidly evolving.
  • Stated: The study fails to provide insights on how GenAI positively influences knowledge attainment because the sample showed high foundational knowledge and literacy for all students. Evident: the sample is limited to marketing students in Melbourne, Australia, and relies on reflections and marks as proxies, which may not fully capture actual learning processes or generalize beyond this context.

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Integrity signals

Registry

Crossref · 10.1080/03075079.2025.2487570

Cites

67 of 68 references verified0 citation errors · 1 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 18 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    When students use GenAI to construct knowledge, this positively affects the demonstration of applied knowledge and critical thinking.

    “Our analysis found that when students use GenAI to construct knowledge, this positively affects the demonstration of applied knowledge and critical thinking.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.
  • Findingown data

    When students use GenAI to regurgitate knowledge, this negatively affects the demonstration of applied knowledge, learning autonomy, and critical thinking.

    “When students used GenAI to regurgitate knowledge, this negatively affects the demonstration of applied knowledge, learning autonomy, and critical thinking.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.
  • Findingown data

    Lower-level learning outcomes result from using GenAI procedurally without augmenting knowledge (procedural approach).

    “In contrast, lower-level learning outcomes resulted from using GenAI procedurally without augmenting knowledge (procedural approach).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Students' personal reflections accurately reflect their learning processes and outcomes.
  • Findingown data

    When students take an augmentative approach to using GenAI in their reflections, their overall marks are significantly higher than when they do not use this approach.

    “Further, when students took an augmentative approach to using GenAI in their reflections, their overall marks were significantly higher to when they did not use this approach.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.
    • Rests on an assumption: Final unit marks and assignment marks are valid outcomes of student learning.
  • Findingown data

    When students use GenAI to regurgitate knowledge in their reflections, their overall grades are significantly lower than if students do not use GenAI in a regurgitative manner.

    “Conversely, we see that when students use GenAI to regurgitate knowledge in their reflections, their overall grades are significantly lower than if students do not use GenAI in a regurgitative manner.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.
    • Rests on an assumption: Final unit marks and assignment marks are valid outcomes of student learning.
  • May combine two claims. 19
  • Classified as probably not a substantial claim. 22

  • May combine two claims.

    Findingown data

    In assessing free-text responses, some students focused on the procedural applications of using GenAI and emphasized how it would provide others with an advantage comparative to themselves.

    “Assessing their free-text responses, we started to see some students focus on the procedural applications of using GenAI and emphasized how it would provide others with an advantage comparative to themselves.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Students' personal reflections accurately reflect their learning processes and outcomes.

  • May combine two claims.

    Findingown data

    Initial pre-study findings found that undergraduate and postgraduate students had differing views on GenAI, with undergraduate students focusing on lack of fairness and postgraduate students recognizing potential for knowledge construction.

    “Our initial pre-study findings found that undergraduate and postgraduate students had differing views when it came to the use of GenAI, with undergraduate students focusing on the lack of fairness, and postgraduate students recognizing the potential for knowledge construction.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Overall, only 6 out of 192 students showed no discipline knowledge and 8 out of 192 showed no information literacy, which prevented analysis of differences in general knowledge attainment.

    “We note that overall, only 6 out of 192 showed no discipline knowledge and 8 out of 192 showed no information literacy, which prevented us from looking at these differences.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Some students acknowledged the potential of GenAI to augment their existing knowledge.

    “On the other hand, some students acknowledged the potential of GenAI to augment their existing knowledge, illustrated through the following comment”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Students' personal reflections accurately reflect their learning processes and outcomes.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The study was submitted and approved through the ethics committees at all involved institutions.

    “This study was submitted and approved through the ethics committees at all involved institutions.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    GenAI has the potential to act as a scaffold mechanism within a course, allowing students to operate within their ZPD.

    “As posed at the beginning of this research, GenAI has the potential to act as a scaffold mechanism within a course, allowing students to operate within their ZPD.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: A mastery goal structure leads to higher learning outcomes.
    • Rests on an assumption: Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Premisereasoning

    GenAI can adapt in real time, based on the prompt given, it is able to adapt as the student's zone shifts.

    “Further to this point, as GenAI can adapt in real time, based on the prompt given, it is able to adapt as the student's zone shifts.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    GenAI can take the place of the instructor supporting students across multiple dimensions of learning and allows for more individualized and rapid feedback helping to bridge the knowledge gap for students when engaging with new learning material.

    “This is relevant in higher education when considering implementing the use of GenAI, as the GenAI tool can take the place of the instructor supporting students across multiple dimensions of learning (Fink 2013) and allows for more individualized and rapid feedback helping to bridge the knowledge gap for students when engaging with new learning material.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Fink 2013

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Given the increase in critical thinking, applied learning, and learning autonomy observed, the applying, analysing, and evaluating levels of Bloom's taxonomy have been obtained.

    “However, given the increase in critical thinking, applied learning, and learning autonomy observed, we believe this demonstrates the applying, analysing, and evaluating levels have been obtained.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: A mastery goal structure leads to higher learning outcomes.
    • Rests on an assumption: Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.

  • Probably not a substantial claim: may not pass S4 (based). May combine two claims.

    Interpretationreasoning

    Both the cognitive orientation and strategic use of GenAI influence the students' goals, performance, and learning outcomes.

    “Both the cognitive orientation and strategic use of GenAI influence the students' goals, performance, and learning outcomes.”
    Quote in the paperNot a substantial claim
    • The authors state this as an assumption: Students adopt performance goals and/or mastery goals to their learning.

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductown data

    Six students did not complete the assessment, leaving 192 reflections able to be included in the analysis.

    “Six students did not complete the assessment, leaving 192 reflections able to be included in the analysis.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    GenAI can enhance critical thinking skills, particularly for tasks at lower cognitive levels such as understanding and application.

    “For example, recent research has found GenAI can enhance critical thinking skills, particularly for tasks at lower cognitive levels such as understanding and application (Essien et al. 2024).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Essien et al. 2024 source

  • May combine two claims.

    Premisecitation

    Mastery goals promote intrinsic motivation and a deeper understanding of the material, leading to long-term academic success.

    “Mastery goals promote intrinsic motivation and a deeper understanding of the material, leading to long-term academic success (Ames 1992;Bieg, Reindl, and Dresel 2017;Huang 2011;Katz-Vago and Benita 2024;Matos et al. 2017).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites Ames 1992 source
    • cites Bieg, Reindl, and Dresel 2017

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Huang 2011 source
    • The authors state this as an assumption: Students adopt performance goals and/or mastery goals to their learning.

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductown data

    The coding process used a consensus-based inter-coding approach, with two authors coding the data and two more researchers checking codes and ratings, and discrepancies resolved through discussion until agreement was achieved.

    “First, two authors coded the data and scored each reflection on whether they demonstrated the code's process/learning/capability to a low, medium or high level.Then these codes and ratings were checked by two more researchers and if there were any discrepancies in interpretations, the research team discussed, using a consensus-based intercoding process, until agreement was achieved.”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Cascio et al. 2019 source
    • cites Friese 2020

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Study design and conductnone given

    The researchers involved in coding were academics with advanced content knowledge specific to each unit.

    “The researchers involved in the coding process were academics with advanced content knowledge that is specific to each of the units involved.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductown data

    Final unit marks and assignment marks were available for each student and were used as the outcome of their learning for the individual task and overall unit performance.

    “For each student, we also had their final unit marks (%) and their assignment marks (%) enabling us to use this as the outcome of their learning for both the individual task and overall unit performance.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Final unit marks and assignment marks are valid outcomes of student learning.

  • May combine two claims.

    Interpretationown data

    Using GenAI to construct and augment human knowledge is associated with higher learning outcomes for students in grades and learning capabilities and capacities, but not for general knowledge attainment.

    “This study shows that using GenAI to construct and augment human knowledge is associated with higher learning outcomes for students in grades and learning capabilities and capacities, but not for general knowledge attainment (due to low variance between students/high knowledge demonstration).”
    Quote in the paperSubstantial claim
    • Rests on an assumption: A mastery goal structure leads to higher learning outcomes.
    • Rests on an assumption: Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.
    • Rests on an assumption: Final unit marks and assignment marks are valid outcomes of student learning.

  • May combine two claims.

    Interpretationown data

    GenAI is not an effective tool for student learning if used in a procedural manner, such as replication and knowledge regurgitation, but it may provide an efficient learning modality if used to help obtain subject mastery.

    “In addition, our study indicates that GenAI isn't an effective tool for student learning if they are using it in a procedural manner, such as replication and knowledge regurgitation, but it may provide an efficient learning modality if used to help obtain subject mastery.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: A mastery goal structure leads to higher learning outcomes.
    • Rests on an assumption: Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    This progression aligns with constructivist principles as students are actively building their knowledge while receiving scaffolded support within their ZPD.

    “This progression aligns with constructivist principles as students are actively building their knowledge while receiving scaffolded support within their ZPD.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Performance goals may hinder deep learning and discourage students from taking risks or exploring beyond their comfort zones.

    “While performance goals can be motivating in the short term, they may hinder deep learning and discourage students from taking risks or exploring beyond their comfort zones (Alhadabi and Karpinski 2020;Huang 2011;Katz-Vago and Benita 2024;Matos et al. 2017).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites Alhadabi and Karpinski 2020

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • cites Huang 2011 source
    • cites Katz-Vago and Benita 2024

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • The authors state this as an assumption: Students adopt performance goals and/or mastery goals to their learning.

  • May combine two claims.

    Interpretationown data

    When students use GenAI in a procedural and regurgitative approach, they show lower learning outcomes and remain within their existing zone of competence.

    “On the other hand, when students use GenAI in a procedural and regurgitative approach, taking information that is generated at face value and not augmenting this with their own understanding, they show lower learning outcomes (as assessed through their achieved grades and learning capacities and capabilities), remaining within their existing zone of competence.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: A mastery goal structure leads to higher learning outcomes.
    • Rests on an assumption: Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.

  • Classified as probably not a substantial claim. May combine two claims.

    Premisenone given

    While the pretest results offer an initial indication of students' GenAI use, they do not provide insights into how the actual use of GenAI influences their learning process and outcomes.

    “While the pretest results offer an initial indication of students' GenAI use, they do not provide insights into how the actual use of GenAI influences their learning process and outcomes.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S4 (based).

    Study design and conductnone given

    Directly asking students about their goal structures may generate socially desirable answers, especially due to perceived power imbalances between the teaching team and students.

    “Directly asking students about their goal structures may generate socially desirable answers, especially due to perceived power imbalances between the teaching team and students.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: Directly asking students about their goal structures may generate socially desirable answers, especially due to perceived power imbalances between the teaching team and students.

  • Probably not a substantial claim: may not pass S2 (checkable), S4 (based).

    Premisenone given

    The unique and complex landscape of GenAI, along with its potential impact on the student experience and implications for learning outcomes, remains largely unknown.

    “However, the unique and complex landscape of GenAI, along with its potential impact on the student experience and implications for learning outcomes, remains largely unknown.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    As identified in Bloom's revised taxonomy, students typically progress through increasingly complex cognitive processes, from basic understanding through to knowledge creation and evaluation.

    “As identified in Bloom's revised taxonomy, students typically progress through increasingly complex cognitive processes, from basic understanding through to knowledge creation and evaluation (Krathwohl 2002).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Krathwohl 2002 source

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Study design and conductcitation

    In line with standard deductive qualitative data analyses practices, the study took a deductive approach to qualitative data analysis and developed a coding framework a priori to analysing the reflections of students on their use of GenAI.

    “In line with standard deductive qualitative data analyses practices (Miles and Huberman 1994), we took a deductive approach to qualitative data analysis and developed a coding framework, a priori to analysing the reflections of students on their use of GenAI.”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Miles and Huberman 1994

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductnone given

    Evidence of augmentative and constructive learning approaches was used as a proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches as a proxy for performance goal adoption.

    “In our study, evidence of augmentative and constructive learning approaches was used as a proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches as a proxy for performance goal adoptions.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    One possible explanation is that when students adopt learning approaches aligning with a mastery goal structure and use GenAI to fill knowledge gaps to create deeper understanding, they achieve better learning outcomes.

    “One possible explanation for this is, when students adopt learning approaches aligning with a mastery goal structure (using AI to construct and augment knowledge) and use GenAI to fill their knowledge gaps to create deeper understanding, they achieve better learning outcomes.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: A mastery goal structure leads to higher learning outcomes.
    • Rests on an assumption: Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.

  • Probably not a substantial claim: may not pass S4 (based).

    Premisenone given

    Existing research in higher education has focused more on the impact of GenAI on academics and institutions than on students.

    “Much of the focus of existing research in the higher education space has considered the impact GenAI has had on academics and institutions.Conversely, research has been less focused on the impact this technology will have on students.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Interpretationown data

    While all students demonstrated proficiency in foundational knowledge, only those who leveraged AI as a constructive and augmentative tool, as opposed to regurgitating AI outputs, progressed to the significant learning levels of application, integration, and learning how to learn.

    “While all students demonstrated proficiency in foundational knowledge, only those who leveraged AI as a constructive and augmentative tool, as opposed to regurgitating AI outputs, progressed to the significant learning levels of application, integration, and learning how to learn.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Fink 2013

      Couldn’t be matched to a paper in Crossref, so it can’t be checked.

    • Rests on an assumption: A mastery goal structure leads to higher learning outcomes.
    • Rests on an assumption: Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.

  • Classified as probably not a substantial claim. May combine two claims.

    Study design and conductreasoning

    Ethical considerations and natural circumstances prevent the study from exposing one group to GenAI while withholding it from another, as GenAI was already omnipresent.

    “Ethical considerations, as well as natural circumstances, prevent us from exposing one group to GenAI while withholding it from another, as GenAI was already omnipresent.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: GenAI was already omnipresent.

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationown data

    The integration of GenAI into higher education requires careful consideration of students' goal structures and learning approaches.

    “Third, they provide empirical evidence that the integration of GenAI into higher education requires careful consideration of students' goal structures and learning approaches.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: A mastery goal structure leads to higher learning outcomes.
    • Rests on an assumption: Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.
32 of 32

  • The authors state this as an assumption.

    Students adopt performance goals and/or mastery goals to their learning.

    “In short, students are assumed to adopt performance goals and/or mastery goals to their learning.”

  • The paper relies on this without support.

    GenAI was already omnipresent.

    “Ethical considerations, as well as natural circumstances, prevent us from exposing one group to GenAI while withholding it from another, as GenAI was already omnipresent.”

  • The paper relies on this without support.

    Directly asking students about their goal structures may generate socially desirable answers, especially due to perceived power imbalances between the teaching team and students.

    “Directly asking students about their goal structures may generate socially desirable answers, especially due to perceived power imbalances between the teaching team and students.”

  • The paper relies on this without support.

    Students' personal reflections accurately reflect their learning processes and outcomes.

    “Students were asked to reflect on their learning experience as part of a formal written assessment task.192 students submitted a personal reflection of how their understanding of the unit topic had evolved over the semester as compared to the original definition generated through ChatGPT-3 (Semester 1, 2023).”

  • The paper relies on this without support.

    A mastery goal structure leads to higher learning outcomes.

    “Existing studies demonstrate that a mastery goal structure leads to higher learning outcomes, and the findings further demonstrate this in the context of the use of GenAI.”

  • The paper relies on this without support.

    Evidence of augmentative and constructive learning approaches is a valid proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches is a valid proxy for performance goal adoption.

    “In our study, evidence of augmentative and constructive learning approaches was used as a proxy for mastery goal adoption, and evidence of regurgitative and procedural learning approaches as a proxy for performance goal adoptions.”

  • The paper relies on this without support.

    Final unit marks and assignment marks are valid outcomes of student learning.

    “For each student, we also had their final unit marks (%) and their assignment marks (%) enabling us to use this as the outcome of their learning for both the individual task and overall unit performance.”

  • The paper relies on this without support.

    Students entering a degree program are expected to have some foundational knowledge and should be able to demonstrate it.

    “The fact that foundational knowledge was demonstrated by all students is somewhat unsurprising, as there is some level of expected knowledge required by students entering a degree program.Therefore, it is implied that students should be able to demonstrate some foundational knowledge.”

  • The paper relies on this without support.

    The three marketing units provide rich and generalizable insights.

    “Similarly, while this research used data and insights from three marketing units, allowing for rich and generalizable insights”
9 of 9

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • The shift from surface-level to deeper critical reflection aligns with emerging research on structured GenAI prompting as a scaffold for higher-order thinking.

    “The shift from surface-level to deeper critical reflection aligns with emerging research on structured GenAI prompting as a scaffold for higher-order thinking”

    From Enhancing Student Success through AI-Assisted Reflective Learning in Entrepreneurship Education: An Empirical Study (2026) · cites this paper as Pallant et al., 2025

    The Practical implications section recommends scaffolding GenAI activities from basic tasks to critical evaluation and teaching students to optimize prompting. The Theoretical implications section links constructive GenAI use with critical thinking. The paper does not establish that structured prompting itself caused a shift from surface-level to deeper critical reflection; its scaffolding recommendation concerns the broader design of learning activities. (LLM, conf=0.83)

    Experimental — accuracy not yet measured

  • There is growing recognition that students themselves need to be actively involved in shaping how GenAI is implemented in education, shifting from passive users to partners.

    “there is growing recognition that students themselves need to be actively involved in this conversation, shifting from being seen as passive users (or risks) to becoming partners in co-creating and harnessing AI for learning (Pallant et al., 2025).”

    From Co-creating custom GPTs: an autoethnographic study of undergraduate students as partners in generative AI innovation (2026) · cites this paper as Pallant et al., 2025

    The Introduction (page unavailable) argues for considering collaboration between students and GenAI rather than treating them as opposing forces. The Practical implications section (page unavailable) encourages students to actively critique and discuss their use of GenAI. These passages support active participation in learning, but not the claim that students should become partners in shaping how GenAI is implemented in education. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • Structured GenAI prompts can help students overcome initial barriers to reflective writing, deepen reflection, and enhance engagement.

    “Recent studies suggest structured GenAI prompts can help students overcome initial barriers to reflective writing, deepen reflection, and enhance engagement (Pallant et al., 2025; Winkler et al., 2023).”

    From Enhancing Student Success through AI-Assisted Reflective Learning in Entrepreneurship Education: An Empirical Study (2026) · cites this paper as Pallant et al., 2025

    The Methodology describes a structured GenAI activity in which students compared their own definitions with AI-generated ones and reflected on their learning. The Practical implications recommend starting with basic interactions to build confidence, then moving to more critical tasks. These passages support scaffolded reflection, but do not establish that prompts overcome initial barriers to reflective writing, deepen reflection itself, or enhance engagement. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • The educational value of generative AI depends less on access alone than on whether students use it to construct and critique knowledge rather than outsource reasoning, and on whether students remain sufficiently risk-aware to avoid academically dishonest or unreflective use.

    “Recent work therefore argues that the educational value of generative AI depends less on access alone than on whether students use it to construct and critique knowledge rather than outsource reasoning, and on whether students remain sufficiently risk-aware to avoid academically dishonest or unreflective use (Pallant et al., 2025; Costa and Murphy, 2025; Ortiz-Bonnin and Blahopoulou, 2025).”

    From College students' metacognitive awareness of generative-AI reliance: an experimental study of decision confidence and attribution bias (2026) · cites this paper as Pallant et al., 2025

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Regurgitation applied-knowledge confidence interval is not centred on its estimate

    Minor

    The reported confidence interval has a midpoint of -1.4585, not the reported log-odds estimate of -2.916. Its positive upper bound also conflicts with the reported p-value of <0.001 for a negative effect.

    Reported -2.916 · recomputed -1.4585

  • Claim of literacy for all students conflicts with reported exceptions

    Minor

    The limitations section says all students showed high levels of literacy, whereas the results say eight of the 192 showed no information literacy.

  • Regurgitation learning-autonomy confidence interval is not centred on its estimate

    Minor

    The interval midpoint is -1.4555 rather than the reported log-odds estimate of -3.114. The interval also includes zero despite the reported p-value of <0.001.

    Reported -3.114 · recomputed -1.4555

  • Regurgitation critical-thinking confidence interval is not centred on its estimate

    Minor

    The interval midpoint is -1.2253, far from the reported log-odds estimate of -3.912. The interval includes zero despite the reported p-value of <0.001.

    Reported -3.912 · recomputed -1.2253

4 of 4

Cites

Flagged first, then by citations

Research works printed but not linked: 2

  1. Fink, L. D. 2013. Creating Significant Learning Outcomes: An Integrated Approach to Designing College Courses. San Francisco, CA: Jossey-Bass.
  2. Svinicki, M. D. 2010. “Fostering a Mastery Goal Orientation in the Classroom.” Essays from Excellence in Teaching 9:25–28.

Cited by

Sorted by citations

Related

Authors
Pallant, J. L., Blijlevens, J., Campbell, A., & Jopp, R.
Affiliations
  • RMIT University
  • Australian Catholic University
  • Swinburne University
ORCID
Venue
Studies in Higher Education
Publisher
Informa UK Limited
Published
2025-05-06
Volume
51
Issue
4
Pages
714-735
ISSN
  • 0307-5079 (ISSN-L)
  • 1470-174X
DOI
10.1080/03075079.2025.2487570
OpenAlex
W4410109561
Cited by
94
References
63
Citations by year
2026: 79 · 2025: 15
FWCI
104.06
Type
journal article
Language
en
Open access
Licence
Locations
Studies in Higher Education (publishedVersion, cc-by-nc-nd)
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Information Systems › Educational Technology in Learning
Keywords
generative AI, learning outcomes, mastery approach, procedural approach, knowledge construction, knowledge improvement, assessment design, curriculum development, student reflection, ChatGPT, higher education

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-10.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-10.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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