The DOI matches its Crossref record; 122 of the 122 references it cites were found in a registry or an index; none of the 108 sources its claims cite is checked for support yet.
The role of ChatGPT in higher education: Benefits, challenges, and future research directions
Open accessAbstract
This paper examines the potential benefits and challenges of using the generative AI model, ChatGPT, in higher education, in the backdrop of the constructivist theory of learning. This perspective-type study presents five benefits of ChatGPT: the potential to facilitate adaptive learning, provide personalised feedback, support research and data analysis, offer automated administrative services, and aid in developing innovative assessments. Additionally, the paper identifies five challenges: academic integrity concerns, reliability issues, inability to evaluate and reinforce graduate skill sets, limitations in assessing learning outcomes, and potential biases and falsified information in information processing. The paper argues that tertiary educators and students must exercise caution when using ChatGPT for academic purposes to ensure its ethical, reliable, and effective use. To achieve this, the paper proposes various propositions, such as prioritising education on the responsible and ethical use of ChatGPT, devising new assessment strategies, addressing bias and falsified information, and including AI literacy as part of graduate skills. By balancing the potential benefits and challenges, ChatGPT can enhance students’ learning experiences in higher education.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
72 Not checked yet · 25 Own interpretation
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By engaging students in conversation and encouraging them to participate in the learning process, ChatGPT can scaffold their prior knowledge and experiences to help them construct new knowledge.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“ChatGPT's logical algorithms that build new knowledge based on existing knowledge align with this approach (Hein, 1991).”
What the sources say
- Not checked yetcites Hein, 1991
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Incorporating ChatGPT into the assessment process can help students construct their knowledge actively.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Additionally, ChatGPT's individualised feedback can support this process by building on their prior knowledge and experiences and providing personalised suggestions for further learning (Ippolito et al., 2022;Vygotsky, 1962).”
What the sources say
- Not checked yetcites Ippolito et al., 2022
- Not checked yetcites Vygotsky, 1962
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, it is clear that ChatGPT, as an AI-powered tool, has the potential to facilitate a constructivism learning experience for students by enabling them to explore and experiment with ideas, ask questions, and receive immediate feedback that allows them to construct their own understanding of knowledge.”
boostedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Specifically, the diagnostic feature of ChatGPT has the potential to serve as the MKO, providing diagnostic individualised (formative) feedback that helps students detect errors and guides them to improve successfully.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“By focusing on authentic assessments, in line with the constructivist theory (Wiggins, 1990), and allowing students to engage with topics they are genuinely interested in, ChatGPT can foster creativity and critical thinking skills (Rudolph et al., 2023a;Dennick, 2016).”
What the sources say
- Not checked yetcites Rudolph et al., 2023a
- Not checked yetcites Dennick, 2016
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Large Learning Models (LLMs) such as ChatGPT provide a scaffolded approach to learning that is consistent with the constructivist theory of learning (Schunk, 2012).”
What the sources say
- Not checked yetcites Schunk, 2012
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“In higher education, large language models like ChatGPT have the potential to greatly assist researchers and students with various tasks, such as efficiently and effectively completing research and writing tasks, including text generation, language translation, and responding to academic queries (Dwivedi et al., 2023;Kasneci et al., 2023;Lund et al., 2023).”
What the sources say
- Not checked yetcites Dwivedi et al., 2023
- Not checked yetcites Kasneci et al., 2023
- Not checked yetcites Lund et al., 2023
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Similar to tools like Leximancer (Smith & Humphreys, 2006), LLMs can also reliably conduct text analysis for sentiment analysis, pattern detection, and emotion detection (Dwivedi et al., 2023;Guo et al., 2023).”
What the sources say
- Not checked yetcites Dwivedi et al., 2023
- Not checked yetcites Guo et al., 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 24
- It may state two separate claims as one. 34
None of the sources cited for this claim have been checked yet.
In the paper
“Furthermore, recent studies have shown that GPT-3 can stimulate curiosity, enhance students' questionasking skills, and generate programming code explanations (Abdelghani et al., 2022;MacNeil et al., 2022).”
What the sources say
- Not checked yetcites Abdelghani et al., 2022
- Not checked yetcites MacNeil et al., 2022
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“It leverages deep learning and powerful algorithms to perform various language-related tasks, such as text generation, question answering, and translation, while comprehending the context to produce responses that resemble human language (Lund et al., 2023).”
What the sources say
- Not checked yetcites Lund et al., 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“This feedback can help students detect errors and guide them towards successful improvement, making ChatGPT an effective”
What the sources say
- Not checked yetcites Geng & Razali, 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Generative Pre-trained Transformer (GPT), which was released by OpenAI (San Francisco, California) in 2018, is a type of Large Language Model (LLM) that aims to replicate human language processing capabilities (Cascella et al., 2023).”
What the sources say
- Not checked yetcites Cascella et al., 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“OpenAI released the ChatGPT-3.5language model family in November 2022 and, subsequently, the ChatGPT-4 family in March 2023 (Skavronskaya et al., 2023).”
What the sources say
- Not checked yetcites Skavronskaya et al., 2023
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“Large language models have become a subject of interest in higher education due to their extensive range of applications, especially as there are now other similar options, such as Bing Chat, Bard, and Ernie (Rudolph et al., 2023b).”
What the sources say
- Not checked yetcites Rudolph et al., 2023b
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“This chatbot can engage in coherent and contextually relevant conversations by responding based on its comprehension of the language and context of the prompts (Gilson et al., 2023;Pavlik, 2023).”
What the sources say
- Not checked yetcites Gilson et al., 2023
- Not checked yetcites Pavlik, 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Academics acknowledge that ensuring the responsible integration of AI into education is critical (Fadel et al., 2019;Polak et al., 2022).”
What the sources say
- Not checked yetcites Fadel et al., 2019
- Not checked yetcites Polak et al., 2022
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
None of the sources cited for this claim have been checked yet.
In the paper
“Overall, incorporating ChatGPT in the learning process enables educators and students to benefit from personalised learning experiences, efficient and effective use of resources, and adaptive learning approaches that enhance the learning outcomes for all, consistent with the principles of constructivist theory (Bada & Olusegun, 2015;Stapleton & Stefaniak, 2019).”
What the sources say
- Not checked yetcites Bada & Olusegun, 2015
- Not checked yetcites Stapleton & Stefaniak, 2019
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Such LLMs can help conduct initial literature reviews, summarise research papers, generate draft versions of research papers (Rahman et al., 2023;Rudolph et al., 2023a), and even assist authors from non-English speaking backgrounds in overcoming language barriers (Gao et al., 2022).”
What the sources say
- Not checked yetcites Rahman et al., 2023
- Not checked yetcites Rudolph et al., 2023a
- Not checked yetcites Gao et al., 2022
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In the higher education sector, innovative assessment activities have gained a lot of attention because they assist students in getting involved with learning resources to think critically and have real learning experiences (Boud & Soler, 2016).”
What the sources say
- Not checked yetcites Boud & Soler, 2016
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, ChatGPT's ability to provide individualised feedback based on the constructivist theory of learning has significant potential to enhance the learning experience and promote successful learning outcomes.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“ChatGPT's research and analysis support, though currently at a basic level, has the potential to fundamentally impact research and higher education, depending on the quality of the prompts (Dwivedi et al., 2023).”
What the sources say
- Not checked yetcites Dwivedi et al., 2023
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Nevertheless, users should exercise caution as ChatGPT may be prone to hallucinations (Alkaissi & McFarlane, 2023) and fabricate references and quotes (Sallam, 2023;Shen et al., 2023).”
What the sources say
- Not checked yetcites Alkaissi & McFarlane, 2023
- Not checked yetcites Sallam, 2023
- Not checked yetcites Shen et al., 2023
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“ChatGPT can act as a research assistant, answering users' questions based on the related literature it has learned (Lin, 2023), and analysing data (Goel, 2020).”
What the sources say
- Not checked yetcites Lin, 2023
- Not checked yetcites Goel, 2020
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Automated administrative support through ChatGPT can use data and analytics to provide timely and personalised non-academic feedback to students, such as notifications about upcoming deadlines, reminders about incomplete tasks, and progress reports on completed tasks, based on individual needs and preferences.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While further research is needed to fully understand the potential of ChatGPT in the tertiary education sector, the constructivist theory of learning supports the use of ChatGPT for automated administrative support as it can facilitate active participation in learning, provide personalised feedback to students, and create a supportive learning environment.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
In the paper
“For academic staff, ChatGPT may be able to summarise and clarify student emails for administrative members to process more efficiently and generate personalised response templates for staff to address students' queries (Dwivedi et al., 2023).”
What the sources say
- Not checked yetcites Dwivedi et al., 2023
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“As indicated earlier, ChatGPT can provide individualised feedback based on the students' prompts, making learning a more rewarding experience (Bridges, 2009;Weldy & Turnipseed, 2010).”
What the sources say
- Not checked yetcites Bridges, 2009
- Not checked yetcites Weldy & Turnipseed, 2010
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“In contributing to a smart learning environment, ChatGPT can utilise big data and learning analytics to monitor student performance, predict success, and respond to students, including their emotional states, in real-time, resulting in personalised adaptive learning (PAL) that is consistent with constructivist theory (Peng et al., 2019;Rudolph et al., 2023a).”
What the sources say
- Not checked yetcites Peng et al., 2019
- Not checked yetcites Rudolph et al., 2023a
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The integration of ChatGPT can assist in creating a supportive learning environment for students by providing timely and accurate information, reducing administrative burdens, and presenting a cost-saving measure for higher education institutions.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“This provides a degree of efficiency and effectiveness, allowing for a synchronous interaction for students (Howlett, 2017;Okonkwo & Ade-Ibijola, 2021) and presenting a cost-savings measure for higher education institutions (Merelo et al., 2022).”
What the sources say
- Not checked yetcites Howlett, 2017
- Not checked yetcites Okonkwo & Ade-Ibijola, 2021
- Not checked yetcites Merelo et al., 2022
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is obvious that the implementation of advanced LLMs like ChatGPT has the potential to revolutionise the tertiary education sector by automating some elements of administrative support and providing a degree of efficiency and effectiveness.”
boostedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: unclear — It is unclear whether it could be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“The conversational nature of LLMs such as ChatGPT facilitates the active construction of students' knowledge as they are continuously engaged with the task, encouraged to find patterns through a scaffolded approach (Stapleton & Stefaniak, 2019), and learn through experimentation and experience, which is an important part of knowledge generation (Rudolph et al., 2023a).”
What the sources say
- Not checked yetcites Stapleton & Stefaniak, 2019
- Not checked yetcites Rudolph et al., 2023a
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“AI applications like ChatGPT can provide accurate and efficient individualised feedback and automated grading, but users need to carefully check the outputs as they depend on the prompts (Rudolph et al., 2023a).”
What the sources say
- Not checked yetcites Rudolph et al., 2023a
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Furthermore, ChatGPT-generated text may contain factual biases due to biased training data, which could perpetuate misconceptions held by learners (Karim, 2023).”
What the sources say
- Not checked yetcites Karim, 2023
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“According to the constructivism learning theory, learning happens best when there are good interactions between the instructor and learner (Schuh, 2003).”
What the sources say
- Not checked yetcites Schuh, 2003
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is no secret that ChatGPT challenges ethical and equity practices in the higher education sector, as it potentially contradicts the constructivist theory of learning that emphasises active student participation and the construction of knowledge.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Constructivist learning theory is the appropriate framework for evaluating ChatGPT's role in higher education.
In the paper
“Using ChatGPT as a tool for exploration and inquiry, students can actively construct their own knowledge and meaning, reducing the likelihood of academic dishonesty, such as plagiarism or cheating (Keith, 2022).”
What the sources say
- Not checked yetcites Keith, 2022
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“ChatGPT can generate initial ideas for assessment design, create multiple-choice or short-answer questions for academics, and produce drafts of case studies or other assessments for further editing (Bridgeman et al., 2023;Liu & Bridgeman, 2023).”
What the sources say
- Not checked yetcites Bridgeman et al., 2023
- Not checked yetcites Liu & Bridgeman, 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Creating authentic learning experiences would require collaborative and consultative learning experiences (Muhajirah, 2020).”
What the sources say
- Not checked yetcites Muhajirah, 2020
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Passive shortcuts, potentially resulting in academic integrity breaches, hinder the active involvement of learners and hence impede learning.”
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Constructivist learning theory is the appropriate framework for evaluating ChatGPT's role in higher education.
In the paper
“Another challenge of ChatGPT is the acceptance of feedback provided by AI rather than human instructors, which is against the constructivist theory of learning that emphasises interactions and social collaboration in learning (Hein, 1991).”
What the sources say
- Not checked yetcites Hein, 1991
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
- Rests on an assumption: Constructivist learning theory is the appropriate framework for evaluating ChatGPT's role in higher education.
In the paper
“However, ChatGPT could impact students' ability to actively construct their own knowledge, as some students may have greater access to ChatGPT than others, resulting in a knowledge gap between students (Hein, 1991).”
What the sources say
- Not checked yetcites Hein, 1991
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The misuse of ChatGPT to create content instantly as a shortcut goes against the philosophy of constructivism and any learning theory, for that matter.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
- Rests on an assumption: Constructivist learning theory is the appropriate framework for evaluating ChatGPT's role in higher education.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Proposition 10 (P10): ChatGPT's ability to develop innovative and authentic student assessments depends on its focus on the work context and the students' existing knowledge.”
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: Constructivist learning theory is the appropriate framework for evaluating ChatGPT's role in higher education.
In the paper
“It can also be integrated into assessment tasks, where students critique generated text or essays and build high-quality articles based on generated drafts, provoking students' existing mental models and developing critical thinking skills (Dennick, 2016).”
What the sources say
- Not checked yetcites Dennick, 2016
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“This technology can be integrated into innovative assessment activities, facilitating collaborative learning, scaffolding, real-time feedback, personalised learning, scalability, interactivity, and fostering knowledge creation and dissipation effectively (Kumar, 2021).”
What the sources say
- Not checked yetcites Kumar, 2021
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Proposition 12 (P12): The integration of ChatGPT in innovative assessment activities can promote critical thinking, problemsolving, and collaboration skills among students.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
- Rests on an assumption: Constructivist learning theory is the appropriate framework for evaluating ChatGPT's role in higher education.
In the paper
“Furthermore, the learning environment includes social groups, instructional strategies, and a motivational atmosphere, to name a few (Zajda, 2021).”
What the sources say
- Not checked yetcites Zajda, 2021
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
In the paper
“Although chatbot technology has shown positive influences on explicit reasoning, learning achievement, knowledge retention, and learning interest, studies have not yet demonstrated significant improvements in critical thinking, learning engagement, and motivation (Deng & Yu, 2023).”
What the sources say
- Not checked yetcites Deng & Yu, 2023
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Using ChatGPT to facilitate learning could lead to unethical and inequitable practices, destroying the spirit of learning (Hein, 1991).”
What the sources say
- Not checked yetcites Hein, 1991
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“However, the constructivist theory of learning suggests that learners actively develop knowledge for themselves through experiences and interactions with others rather than passively acquiring it through external tools like ChatGPT (Geary, 1995).”
What the sources say
- Not checked yetcites Geary, 1995
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, incorporating AI literacy as part of graduate skills could enhance graduates' employability and preparedness for the rapidly evolving job market.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Proposition 18 (P18): The use of ChatGPT in learning and assessment can impact the development of graduate skills, such as critical thinking and problem-solving.”
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Graduate skills, including critical thinking and problemsolving, communication, collaboration and teamwork, leadership, adaptability, digital literacy, global and cultural awareness, ethics, and professionalism, are essential for future professional and personal success (Abelha et al., 2020;Osmani et al., 2019;Oliver & de St Jorre, 2018; University of Adelaide, 2022; University of Sydney, 2022).”
What the sources say
- Not checked yetcites Abelha et al., 2020
- Not checked yetcites Osmani et al., 2019
- Not checked yetcites Oliver & de St Jorre, 2018
plain- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This could also make it challenging to evaluate the effectiveness of group learning activities and assess students' ability to work collaboratively.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“However, the use of ChatGPT for assessment is a passive process and does not allow for social interaction, hindering students' ability to construct meaning through reflection on their experiences (Biggs, 2014).”
What the sources say
- Not checked yetcites Biggs, 2014
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“The use of ChatGPT presents an opportunity to incorporate artificial intelligence literacy as part of graduate skills, preparing graduates for effective workplace application of large language models that may replace some existing jobs and create new ones (Cradle, 2023).”
What the sources say
- Not checked yetcites Cradle, 2023
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Therefore, the incorporation of these methods into the curriculum and assessment could promote the development of graduate skills beyond the use of ChatGPT alone.”
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence.
- Rests on an assumption: Constructivist learning theory is the appropriate framework for evaluating ChatGPT's role in higher education.
The paper relies on this without support.
LLMs like ChatGPT are ubiquitous in higher education.
“The ubiquity of LLMs like ChatGPT has prompted a reevaluation of assessment design, with a focus on fostering creativity, critical thinking, authenticity, practicality, and collaboration (Nieminen et al., 2022;Villarroel et al., 2018), aligning with the constructivist theory's emphasis on authentic and formative assessment (Wiggins, 1990;Black & Wiliam, 2009).”
The paper relies on this without support.
Constructivist learning theory is the appropriate framework for evaluating ChatGPT's role in higher education.
“The paper presented five challenges and five benefits of ChatGPT for the higher education sector in the backdrop of the constructivism learning theory (Figure 1).”
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 cited source says what the claim attributes to it.
In the citing paper
“However, this shift toward individual learning raises important considerations for business education, where teamwork and interpersonal skills are crucial for professional success [15].”
From Scaffolding Creativity: Integrating Generative AI Tools and Real-World Experiences in Business Education (2025) · cites this paper as [15]
The cited source says what the claim attributes to it.
In the citing paper
“The arrival of AI in education has potential to produce significant transformations in the teacher-student relationship [54].”
From New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution (2023) · cites this paper as [54]
The cited source says what the claim attributes to it.
In the citing paper
“The diverse learning trajectories observed in these cases illustrate how AI tools can facilitate non-linear, adaptive learning processes by enabling students to dynamically integrate new information with prior knowledge and external resources [15, 16].”
From Scaffolding Creativity: Integrating Generative AI Tools and Real-World Experiences in Business Education (2025) · cites this paper as [15]
In the citing paper
“ChatGPT has the potential to offer personalized learning experiences and support an effective learning process by providing students with customized feedback and explanations tailored to their needs and abilities”
From A systematic literature review of empirical research on ChatGPT in education (2024) · cites this paper as [60]
In the citing paper
“Recent studies have brought to light the potential for students to exploit ChatGPT for academic dishonesty.”
From ChatGPT for Academic Purposes: Survey Among Undergraduate Healthcare Students in Malaysia (2024) · cites this paper as [6]
In the citing paper
“The primary concern lies in the potential compromise to academic integrity presented by AI essay-writing systems, particularly ChatGPT.”
From ChatGPT for Academic Purposes: Survey Among Undergraduate Healthcare Students in Malaysia (2024) · cites this paper as [6]
In the citing paper
“There is an apprehension that students might submit essays that do not authentically represent their work, thus contravening ethical standards within educational institutions [4,6].”
From ChatGPT for Academic Purposes: Survey Among Undergraduate Healthcare Students in Malaysia (2024) · cites this paper as [6]
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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Evaluate graduate skill sets
“(Fosnot, 1996)”
§ References
“40. Fosnot, C. T. (2013). Constructivism: Theory, perspectives, and practice. Teachers College Press.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Constructivist theory of learning
“(Taber, 2011)”
§ References
“125. Taber, K. S. (2012). Constructivism as educational theory: Contingency in learning, and optimally guided instruction.”
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Adaptive learning
“(White et al., 2014;Kasneci et al., 2023)”
§ References
“139. White, P. J., Larson, I., Styles, K., Yuriev, E., Evans, D. R., Rangachari, P. K., Short, J. L., Exintaris, B., Malone, D. T., Davie, B., Eise, N., McNamara, K., & Naidu, S. (2016).”
Citations
Cites
Yaşlı Kadınlarda Üreme Sağlığı
Kokanalı, D., & Engin Üstün, Y. (n.d.) · DergiPark (Istanbul University) · Cited by 170,090
Hattie, J., & Timperley, H. (2007) · Review of Educational Research · doi:10.3102/003465430298487 · Cited by 12,905
ChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 24×
“LLMs like ChatGPT can also be incorporated into assessment tasks as text-generators, with students tasked to critically evaluate the generated output (Monash University, 2023).”
BackgroundInfluentialZawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · cited 2×
“Nonetheless, adaptive learning systems aim to transform students from passive recipients to active collaborators through a scaffolded approach to learning (Deng & Yu, 2023; Zawacki-Richter et al., 2019).”
Background(2023) · doi:10.1126/science · Cited by 6,063
Nicol, D. J., & Macfarlane‐Dick, D. (2006) · Studies in Higher Education · doi:10.1080/03075070600572090 · Cited by 5,846
Signals from Chloroplasts Converge to Regulate Nuclear Gene Expression
Koussevitzky, S., Nott, A., Mockler, T. C., Hong, F., Sachetto‐Martins, G., Surpin, M. A., Lim, J. C.-S., Mittler, R., & Chory, J. (2007) · Science · doi:10.1126/science.1140516 · Cited by 5,765
Shute, V. J. (2008) · Review of Educational Research · doi:10.3102/0034654307313795 · Cited by 4,631
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., Baabdullah, A. M., Koohang, A., Raghavan, V., Ahuja, M., et al. (2023) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2023.102642 · Cited by 4,584
Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115
Learning Theories: An Educational Perspective
Schunk, D. H. (1991) · Cited by 3,725
Developing the theory of formative assessment
Black, P., & Wiliam, D. (2009) · Educational Assessment, Evaluation and Accountability · doi:10.1007/s11092-008-9068-5 · Cited by 3,301 · cited 2×
“Drawing from the constructivist theory, which emphasises the importance of authentic assessment and formative feedback (Schunk, 2012; Wiggins, 1990; Black & Wiliam, 2009), different approaches have been suggested in previous literature, such as the implementation of e-portfolios to facilitate…”
BackgroundMethodSallam, M. (2023) · Healthcare · doi:10.3390/healthcare11060887 · Cited by 3,126
Chatting and cheating: Ensuring academic integrity in the era of ChatGPT
Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2190148 · Cited by 3,087
Chatting and Cheating. Ensuring academic integrity in the era of ChatGPT
Cotton, D., Cotton, P., & Shipway, J. R. (2023) · doi:10.35542/osf.io/mrz8h · Cited by 3,079
Artificial Intelligence and Management: The Automation–Augmentation Paradox
Raisch, S., & Krakowski, S. (2021) · Academy of Management Review · doi:10.5465/amr.2018.0072 · Cited by 2,755
Gilson, A., Safranek, C. W., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2023) · JMIR Medical Education · doi:10.2196/45312 · Cited by 2,078
Exploring the impact of artificial intelligence on teaching and learning in higher education
Popenici, S. A. D., & Kerr, S. (2017) · Research and Practice in Technology Enhanced Learning · doi:10.1186/s41039-017-0062-8 · Cited by 2,019
(1996) · Medical Entomology and Zoology · Cited by 2,012
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00237-x · Cited by 2,003 · cited 1×
“Tlili et al. (2023) addressed early adopters’ experience in education.”
ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.9 · Cited by 1,806
Artificial hallucinations in ChatGPT: implications in scientific writing
Alkaissi, H., & McFarlane, S. I. (2023) · Cureus · doi:10.7759/cureus.35179 · Cited by 1,540
A SWOT analysis of ChatGPT: Implications for educational practice and research
Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2023.2195846 · Cited by 1,384
Chatting about ChatGPT: how may AI and GPT impact academia and libraries?
Lund, B. D., & Wang, T. (2023) · Library Hi Tech News · doi:10.1108/lhtn-01-2023-0009 · Cited by 1,355
Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023) · The International Journal of Management Education · doi:10.1016/j.ijme.2023.100790 · Cited by 1,261
Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping
Smith, A. E., & Humphreys, M. S. (2006) · Behavior Research Methods · doi:10.3758/bf03192778 · Cited by 1,206
Cascella, M., Montomoli, J., Bellini, V., & Bignami, E. (2023) · Journal of Medical Systems · doi:10.1007/s10916-023-01925-4 · Cited by 1,120
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2022) · doi:10.1101/2022.12.23.521610 · Cited by 1,099
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00819-6 · Cited by 1,099
ChatGPT and other large language models are double-edged swords
Shen, Y., Heacock, L., Elias, J., Hentel, K. D., Reig, B., Shih, G., & Moy, L. (2023) · Radiology · doi:10.1148/radiol.230163 · Cited by 1,008
(2019) · Open Research Online (The Open University) · Cited by 1,004
Pavlik, J. V. (2023) · Journalism & Mass Communication Educator · doi:10.1177/10776958221149577 · Cited by 972 · cited 2×
“This chatbot can engage in coherent and contextually relevant conversations by responding based on its comprehension of the language and context of the prompts (Gilson et al., 2023; Pavlik, 2023).”
BackgroundLund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24750 · Cited by 919
Chatbots applications in education: A systematic review
Okonkwo, C. W., & Ade-Ibijola, A. (2021) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2021.100033 · Cited by 879
ChatGPT in higher education: considerations for academic integrity and student learning
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.17 · Cited by 853 · cited 2×
“Therefore, to maintain academic integrity while using ChatGPT, responsible and ethical use of information generated by the model is necessary (Keith, 2022; Sullivan et al., 2023).”
MethodFactors affecting students’ learning performance through collaborative learning and engagement
Qureshi, M. A., Khaskheli, A., Qureshi, J. A., Raza, S. A., & Yousufi, S. Q. (2021) · Interactive Learning Environments · doi:10.1080/10494820.2021.1884886 · Cited by 822
The impact of artificial intelligence on learner–instructor interaction in online learning
Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00292-9 · Cited by 788
Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023) · Information Systems Frontiers · doi:10.1007/s10796-022-10291-4 · Cited by 685
Abelha, M., Fernandes, S. R. G., Mesquita, D., Seabra, F., & Ferreira-Oliveira, A. T. (2020) · Sustainability · doi:10.3390/su12155900 · Cited by 607
Sustainable assessment revisited
Boud, D., & Soler, R. (2016) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2015.1018133 · Cited by 576
Authentic assessment: creating a blueprint for course design
Villarroel, V., Bloxham, S., Bruna, D., Bruna, C., & Herrera-Seda, C. (2018) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2017.1412396 · Cited by 560
Peng, H., Ma, S., & Spector, J. M. (2019) · Smart Learning Environments · doi:10.1186/s40561-019-0089-y · Cited by 548
Crawford, J., Cowling, M., & Allen, K.-A. (2023) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.3.02 · Cited by 547 · cited 1×
“Assessment designs should engage students with tasks that require critical thinking and cannot be easily replicated by LLMs (Crawford et al., 2023; Kuhn, 2019; Iordanou et al., 2019).”
BackgroundCognitive Psychology and Instruction
(1978) · Springer eBooks · doi:10.1007/978-1-4684-2535-2 · Cited by 540
(2008) · Cited by 536
Geary, D. C. (1995) · American Psychologist · doi:10.1037//0003-066x.50.1.24 · Cited by 511
The Case for Authentic Assessment
Wiggins, G. (1990) · University of Massachusetts Amherst · doi:10.7275/ffb1-mm19 · Cited by 493
“What Can ChatGPT Do?” Analyzing Early Reactions to the Innovative AI Chatbot on Twitter
Taecharungroj, V. (2023) · Big Data and Cognitive Computing · doi:10.3390/bdcc7010035 · Cited by 489
Rediscovering the use of chatbots in education: A systematic literature review
Pérez, J. Q., Daradoumis, T., & Puig, J. M. M. (2020) · Computer Applications in Engineering Education · doi:10.1002/cae.22326 · Cited by 483
Constructive alignment in university teaching
Kandlbinder, P. (2014) · Cited by 479
Tam, M. (2000) · Cited by 478
ChatGPT for education and research: A review of benefits and risks
Sok, S., & Heng, K. (2023) · Cambodian Journal of Educational Research · doi:10.62037/cjer.2023.03.01.06 · Cited by 475
Falchikov, N. (2005) · Medical Entomology and Zoology · Cited by 449
What ChatGPT means for universities: Perceptions of scholars and students
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.22 · Cited by 431 · cited 3×
“…to aid in higher education by providing assistance with research, analysis, and writing tasks, concerns surrounding potential bias and falsified information need to be addressed to ensure its use is ethical and reliable (Dwivedi et al., 2023; Firat, 2023; Gatzemeier, 2021; Silberg & Manyika, 2019).”
Background(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.23 · Cited by 421
Flanagin, A., Bibbins-Domingo, K., Berkwits, M., & Christiansen, S. L. (2023) · JAMA · doi:10.1001/jama.2023.1344 · Cited by 420
ChatGPT for Education and Research: A Review of Benefits and Risks
Sok, S., & Heng, K. (2023) · doi:10.2139/ssrn.4378735 · Cited by 386
Australian code for the responsible conduct of research
Australia, U. (2018) · Cited by 385
Liu, O. L., Frankel, L., & Roohr, K. C. (2014) · ETS Research Report Series · doi:10.1002/ets2.12009 · Cited by 340
Going for a walk: a verbatim play
Heddon, D. E. (2015) · ENLIGHTEN (Jurnal Bimbingan dan Konseling Islam) · doi:10.1080/14 · Cited by 337
How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection
Guo, B., Zhang, X., Wang, Z., Jiang, M., Nie, J., Ding, Y., Yue, J., & Wu, Y. (2023) · arXiv · doi:10.48550/arxiv.2301.07597 · Cited by 318
Deng, X., & Yu, Z. (2023) · Sustainability · doi:10.3390/su15042940 · Cited by 298 · cited 2×
“Nonetheless, adaptive learning systems aim to transform students from passive recipients to active collaborators through a scaffolded approach to learning (Deng & Yu, 2023; Zawacki-Richter et al., 2019).”
BackgroundChatGPT and Academic Research: A Review and Recommendations Based on Practical Examples
Rahman, M. M., Terano, H. J. R., Rahman, M. N., Salamzadeh, A., & Rahaman, M. S. (2023) · Journal of Education Management and Development Studies · doi:10.52631/jemds.v3i1.175 · Cited by 277
Constructivist Learning Theory and Creating Effective Learning Environments
Zajda, J. (2021) · Globalisation, comparative education and policy research · doi:10.1007/978-3-030-71575-5_3 · Cited by 270
Graduate attributes for 2020 and beyond: recommendations for Australian higher education providers
Oliver, B., & Jorre, T. J. D. S. (2018) · Higher Education Research & Development · doi:10.1080/07294360.2018.1446415 · Cited by 266
Critical Thinking as Discourse
Kuhn, D. (2019) · Human Development · doi:10.1159/000500171 · Cited by 265
Dennick, R. (2016) · International Journal of Medical Education · doi:10.5116/ijme.5763.de11 · Cited by 242
Why and how to embrace AI such as ChatGPT in your academic life
Lin, Z. (2023) · doi:10.31234/osf.io/sdx3j · Cited by 202
Why and how to embrace AI such as ChatGPT in your academic life
Lin, Z. (2023) · Royal Society Open Science · doi:10.1098/rsos.230658 · Cited by 202
Generating Diverse Code Explanations using the GPT-3 Large Language Model
MacNeil, S., Tran, A., Mogil, D., Bernstein, S., Ross, E., & Huang, Z. (2022) · Proceedings of the 2022 ACM Conference on International Computing Education Research - Volume 2 · doi:10.1145/3501709.3544280 · Cited by 200
Kumar, J. A. (2021) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-021-00302-w · Cited by 183
New Modes of Learning Enabled by AI Chatbots: Three Methods and Assignments
Mollick, E. R., & Mollick, L. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4300783 · Cited by 176 · cited 1×
“ChatGPT has been recognised for its ability to develop assessment questions, lesson plans, and curricula in higher education (Dwivedi et al., 2023; Mollick & Mollick, 2022).”
BackgroundSkavronskaya, L., Hadinejad, A., & Cotterell, D. (2023) · Journal of Teaching in Travel & Tourism · doi:10.1080/15313220.2023.2196658 · Cited by 174
Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity
Zhuo, T. Y., Huang, Y., Chen, C., & Xing, Z. (2023) · arXiv · doi:10.48550/arxiv.2301.12867 · Cited by 164
ChatGPT for Higher Education and Professional Development: A Guide to Conversational AI
Atlas, S. (2023) · Journal of Media Literacy Education · Cited by 144
GPTutor: A ChatGPT-Powered Programming Tool for Code Explanation
Chen, E., Huang, R., Chen, H.-S., Tseng, Y., & Li, L. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-36336-8_50 · Cited by 142
Towards the mature ePortfolio: Some implications for higher education
Challis, D. (2005) · Canadian Journal of Learning and Technology · doi:10.21432/t2ms41 · Cited by 136
Student-centred learning: a humanist perspective
Tangney, S. (2013) · Teaching in Higher Education · doi:10.1080/13562517.2013.860099 · Cited by 135
Teachers’ perspective on artificial intelligence education: An initial investigation
Polak, S., Schiavo, G., & Zancanaro, M. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts · doi:10.1145/3491101.3519866 · Cited by 132
Designing the digital in authentic assessment: is it fit for purpose?
Nieminen, J. H., Bearman, M., & Ajjawi, R. (2022) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2022.2089627 · Cited by 131
Ma, L. (2021) · Mobile Information Systems · doi:10.1155/2021/2637439 · Cited by 127
Kerr, P. (2015) · ELT Journal · doi:10.1093/elt/ccv055 · Cited by 125
Online chat and chatbots to enhance mature student engagement in higher education
Abbas, N., Whitfield, J., Atwell, E., Bowman, H., Pickard, T., & Walker, A. (2022) · International Journal of Lifelong Education · doi:10.1080/02601370.2022.2066213 · Cited by 116
Graduates employability skills: A review of literature against market demand
Osmani, M., Weerakkody, V., Hindi, N. M., & Eldabi, T. (2019) · Journal of Education for Business · doi:10.1080/08832323.2018.1545629 · Cited by 104
White, P. J., Larson, I., Styles, K., Yuriev, E., Evans, D. R., Rangachari, P. K., Short, J. L., Exintaris, B., Malone, D. T., Davie, B., et al. (2016) · Higher Education Research & Development · doi:10.1080/07294360.2015.1107887 · Cited by 102
Assessing the quality of student-generated short answer questions using GPT-3
Moore, S., Nguyen, H. A., Bier, N., Domadia, T., & Stamper, J. (2022) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-16290-9_18 · Cited by 93
Will AI Console Me when I Lose my Pet? Understanding Perceptions of AI-Mediated Email Writing
Liu, Y., Mittal, A., Yang, D., & Bruckman, A. (2022) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3491102.3517731 · Cited by 92
Designing assessment for inclusion: an exploration of diverse students’ assessment experiences
Tai, J., Dollinger, M., Ajjawi, R., Jorre, T. J. D. S., Krattli, S. N., McCarthy, D., & Prezioso, D. (2022) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2022.2082373 · Cited by 91
Knowledge construction in the learner-centered classroom.
Schuh, K. L. (2003) · Journal of Educational Psychology · doi:10.1037/0022-0663.95.2.426 · Cited by 89
Iordanou, K., Kuhn, D., Matos, F. A., Shi, Y., & Hemberger, L. (2019) · Learning and Instruction · doi:10.1016/j.learninstruc.2019.05.004 · Cited by 82
Machine learning based feedback on textual student answers in large courses
Bernius, J. P., Krusche, S., & Bruegge, B. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100081 · Cited by 75
The S treisand effec t and censorship backfir e
Jansen, S. C., & Martin, B. G. (2015) · International journal of communication · Cited by 73
(2012) · Cited by 70
Constructivist Learning Theory
(2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_2097 · Cited by 67
Chatbots and messaging platforms in the classroom: An analysis from the teacher’s perspective
Merelo, J. J., Castillo, P. Á., Mora, A. M., Barranco, F., Abbas, N., Guillén, A., & Tsivitanidou, O. (2023) · Education and Information Technologies · doi:10.1007/s10639-023-11703-x · Cited by 63
Assessing and Improving Learning in Business Schools: Direct and Indirect Measures of Learning
Weldy, T. G., & Turnipseed, D. L. (2010) · Journal of Education for Business · doi:10.1080/08832320903449535 · Cited by 62
Advances in research on classroom dialogue: learning outcomes and assessments
van der Veen, C., & van Oers, B. (2017) · Learning and Instruction · doi:10.1016/j.learninstruc.2017.04.002 · Cited by 61
Muhajirah, M. (2020) · International Journal of Asian Education · doi:10.46966/ijae.v1i1.23 · Cited by 61
Ways of Learning: Learning Theories for the Classroom
Pritchard, A. (2017) · Cited by 59
Nautiyal, R., Albrecht, J. N., & Nautiyal, A. (2023) · Annals of Tourism Research · doi:10.1016/j.annals.2023.103544 · Cited by 56
AI-Powered Learning: Making Education Accessible, Affordable, and AchievablePreprint
Goel, A. K. (2020) · arXiv.org · doi:10.48550/arxiv.2006.01908 · Cited by 53
Creative Writing with an AI-Powered Writing Assistant: Perspectives from Professional Writers
Ippolito, D., Yuan, A., Coenen, A., & Burnam, S. (2022) · arXiv · doi:10.48550/arxiv.2211.05030 · Cited by 52
Quiz maker: Automatic quiz generation from text using NLP
Gabajiwala, E., Mehta, P., Singh, R., & Koshy, R. (2022) · Lecture Notes in Electrical Engineering · doi:10.1007/978-981-19-5037-7_37 · Cited by 50
Research quality assessment in education: Impossible science, possible art?
Bridges, D. (2009) · British Educational Research Journal · doi:10.1080/01411920903111565 · Cited by 47
Cognitive Constructivism: Revisiting Jerome Bruner’s Influence on Instructional Design Practices
Stapleton, L., & Stefaniak, J. E. (2018) · TechTrends · doi:10.1007/s11528-018-0356-8 · Cited by 40
Jingxin, G., & Razali, A. B. (2020) · Universal Journal of Educational Research · doi:10.13189/ujer.2020.082638 · Cited by 37
Hartmann, J., Schwenzow, J., & Witte, M. (2023) · arXiv · doi:10.48550/arxiv.2301.01768 · Cited by 37 · cited 2×
“Potential bias and falsified information in information processing Large language model use, including ChatGPT, in tertiary education presents challenges due to the potential introduction of bias and falsified information in information processing (Chen et al., 2023; Hartmann et al., 2023).”
BackgroundMethodZhao, X., Shao, M., & Su, Y.-S. (2022) · Sustainability · doi:10.3390/su141710699 · Cited by 30
Use of technology in constructivist approach
IŞIK, A. D. (2018) · Educational Research and Reviews · doi:10.5897/err2018.3609 · Cited by 22
Mere exposure to dialogic framing enriches argumentive thinking
Kuhn, D., & Modrek, A. (2021) · Applied Cognitive Psychology · doi:10.1002/acp.3862 · Cited by 19
GPT-3-driven pedagogical agents for training children's curious question-asking skills
Abdelghani, R., Wang, Y.-H., Yuan, X., Wang, T., Lucas, P., Sauzéon, H., & Oudeyer, P.-Y. (2022) · arXiv · doi:10.48550/arxiv.2211.14228 · Cited by 19
Higher Education at the Crossroads of Disruption: The University of the 21st Century
Kaplan, A. (2021) · Cited by 17
GPTutor: a ChatGPT-powered programming tool for code explanationPreprint
Chen, E., Huang, R., Chen, H.-S., et al. (2023) · arXiv (Cornell University) · doi:10.48550/arxiv.2305.01863 · Cited by 16
Kuo, Y., & Chang, Y. (2022) · Education and Information Technologies · doi:10.1007/s10639-022-11540-4 · Cited by 14
Assessment to Support Student Learning: Eight Challenges for 21st Century Practice.
Sambell, K., Brown, S. A., & Race, P. (2019) · AISHE-J · doi:10.62707/aishej.v11i2.414 · Cited by 12
How machine learning is developing to get more insight from complex voice-of-customer data
Howlett, N. (2017) · Applied marketing analytics · doi:10.69554/xvbz1375 · Cited by 12
Factors Influencing Students' Continuous Willingness to Use E-Learning Platforms in Higher Education
Li, Z. (2022) · International Journal of Information and Communication Technology Education · doi:10.4018/ijicte.313424 · Cited by 10
İnternet Destekli Yapıcı Öğrenme Ortamları
Kılıç, E., Karadeniz, Ş., & Karataş, S. (2003) · Gazi Üniversitesi Gazi Eğitim Fakültesi Dergisi · doi:10.17152/gefd.69430 · Cited by 7
Chatbots and messaging platforms in the classroom: an analysis from the teacher's perspective
Merelo, J. J., Castillo, P. A., Mora, A. M., et al. (2022) · White Rose Research Online (University of Leeds, The University of Sheffield, University of York) · doi:10.48550/arxiv.2201.10289 · Cited by 2
Research works printed but not linked: 13
- Adar, N., & Kandemir M. C (2008, May 8). M-learning tools for palm device: M-test and m-exercise [Paper presentation]. 8th International Educational Technology Conference, Anadolu University, Eskisehir.
- Bada, S. O., & Olusegun, S. (2015). Constructivism learning theory: A paradigm for teaching and learning. Journal of Research & Method in Education, 5(6), 66-70.
- Bruner, J. S. (1961). The act of discovery. Harvard Educational Review, 31, 21-32.
- Dijkstra, R., Genç, Z., Kayal, S., & Kamps, J. (2022, July 27-31). Reading comprehension quiz generation using generative pre-trained transformers [Paper presentation]. The 23th International Conference on Artificial Intelligence in Education (AIED' 2022).
- Jansen, S. C., & Martin, B. (2015). The Streisand effect and censorship backfire. International Journal of Communication, 9, 656-671.
- June, S., Yaacob, A., & Kheng, Y. K. (2014). Assessing the use of YouTube videos and interactive activities as a critical thinking stimulator for tertiary students: An action research. International Education Studies, 7(8), 56-67. https://doi. org/10.5539/ies. · DOI
- Kılıç, E., Karadeniz, Ş., & Karataş, S. (2003). İnternet destekli yapici ogrenme ortamlari. Gazi University Journal of Gazi Educational Faculty, 23(2), 149-160.
- Makewa, L. N. (2019). Constructivist theory in technology- based learning. In L. N. Makewa, B. M. Ngussa, & J. M. Kuboja (Eds.), Technology-supported teaching and research methods for educators (pp. 268-287). IGI Global.
- Piaget, J. (1980). The psychogenesis of knowledge and its epistemological significance. In M. Piatelli-Palmarini (Ed.), Language and learning: The debate between Jean Piaget and Noam Chomsky (pp. 1-23). Harvard University Press.
- Qiu, J. (2019). A preliminary study of English mobile learning model based on constructivism. Theory and Practice in Language Studies, 9(9), 1167-1172. http://dx.doi. org/10.17507/tpls.0909. · DOI
- In J. Hassaskhah (Ed.), Educational theory (pp. 39-61). Nova.
- Vygotsky, L. S. (1962). Thought and language. MIT Press.
- Yang, T. C., Hwang, G. J., & Yang, S. J. H. (2013). Development of an adaptive learning system with multiple perspectives based on students' learning styles and cognitive styles. Journal of Educational Technology & Society, 16(4), 185-200.
Cited by
Sorted by citations
New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023) · Sustainability · doi:10.3390/su151612451 · Cited by 1,089
Imran, M., & Almusharraf, N. (2023) · Contemporary Educational Technology · doi:10.30935/cedtech/13605 · Cited by 421
AI literacy and its implications for prompt engineering strategies
Knoth, N., Tolzin, A., Janson, A., & Leimeister, J. M. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100225 · Cited by 368
“…ChatGPT ’ s, capacity to enrich the educational experience by supporting a wide range of learning methodologies, including adaptive learning, personalized learning, and self-directed learning (Rahman & Watanobe, 2023; Rasul et al., 2023; Ruwe & Mayweg-Paus, 2023; Zhu, Sun, Luo, Li, & Wang, 2023).”
BackgroundWang, H., Dang, A., Wu, Z., & Mac, S. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100326 · Cited by 272
“Then we performed a systematic search using”
BackgroundHigher education students’ perceptions of ChatGPT: A global study of early reactions
Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., A. Iahad, N., Abdulla, A. A., Akopyan, A., Aldana Segura, M. W., AlHumaid, J., et al. (2025) · PLOS ONE · doi:10.1371/journal.pone.0315011 · Cited by 376
Metadata
- Authors
- Rasul, T., Nair, S., Kalendra, D. R., Robin, M., Santini, F. D. O., Ladeira, W. J., Sun, M., Day, I., Rather, R. A., & Heathcote, L.
- Affiliations
- Australian Institute of Business (AU)
- Universidade do Vale do Rio dos Sinos (BR)
- ORCID
- Venue
- Journal of Applied Learning & Teaching
- Publisher
- Open Access Publishing Association
- Published
- 2023-05-11
- Volume
- 6
- Issue
- 1
- ISSN
- 2591-801X (ISSN-L)
- OpenAlex
- W4376139682
- Semantic Scholar
- 9e7cd4ce1ed4b842e8a428c3774bd06ca3a5b769
- Cited by
- 561
- References
- 122
- Citations by year
- 2026: 69 · 2025: 235 · 2024: 209 · 2023: 47
- FWCI
- 131.92
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Journal of Applied Learning & Teaching (publishedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Keywords
- ChatGPT, generative AI in education, higher education, constructivist learning theory, adaptive learning, personalized feedback, academic integrity, AI evaluation, AI literacy, algorithmic bias, ethical use of AI, graduate competencies
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.
A language model drew the claims from the paper on 2026-10-05, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-05 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
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 ›
Are you an author, or do you see a problem with this page? Request a review
