The DOI matches its Crossref record; 63 of the 64 references it cites were found in a registry or an index; none of the 30 sources its claims cite is checked for support yet.
Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models
Open accessAbstract
Advances in language modeling have paved the way for novel human-AI co-writing experiences. This paper explores how varying levels of scaffolding from large language models (LLMs) shape the co-writing process. Employing a within-subjects field experiment with a Latin square design, we asked participants (N=131) to respond to argumentative writing prompts under three randomly sequenced conditions: no AI assistance (control), next-sentence suggestions (low scaffolding), and next-paragraph suggestions (high scaffolding). Our findings reveal a U-shaped impact of scaffolding on writing quality and productivity (words/time). While low scaffolding did not significantly improve writing quality or productivity, high scaffolding led to significant improvements, especially benefiting non-regular writers and less tech-savvy users. No significant cognitive burden was observed while using the scaffolded writing tools, but a moderate decrease in text ownership and satisfaction was noted. Our results have broad implications for the design of AI-powered writing tools, including the need for personalized scaffolding mechanisms.
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53 Own result · 20 Own method · 18 Not checked yet · 13 Own interpretation · 2 Sources not found · 2 No basis given
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but a moderate decrease in text ownership and satisfaction was noted.”
- Also stated in: § 1 INTRODUCTION · plain
“However, this enhanced writing experience was accompanied by a notable caveat: a moderate decrease in perceived text ownership and overall satisfaction among users of scaffolded writing tools.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > Abstract
plaincorrelationaln = 131- Asserted: passes — The authors put it forward as true.
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- Also stated in: § 1 INTRODUCTION · plain
A result the paper reports from its own data — 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 low scaffolding did not significantly improve writing quality or productivity, high scaffolding led to significant improvements, especially benefiting non-regular writers and less tech-savvy users.”
- Also stated in: § 1 INTRODUCTION · plain
“Notably, low-level scaffolding-defined as next-sentence suggestions-did not significantly enhance these outcomes.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > Abstract
plainn = 131- Asserted: passes — The authors put it forward as true.
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- Also stated in: § 1 INTRODUCTION · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“No significant cognitive burden was observed while using the scaffolded writing tools, but a moderate decrease in text ownership and satisfaction was noted.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > Abstract
plainn = 131- Asserted: passes — The authors put it forward as true.
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In the paper
“Our findings reveal a U-shaped impact of scaffolding on writing quality and productivity (words/time).”
- Also stated in: § 1 INTRODUCTION · plain
“Our empirical findings reveal a complex, U-shaped relationship between the level of AI scaffolding in writing and the metrics of writing quality and productivity (words/time).”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > Abstract
plaindirectn = 131- Asserted: passes — The authors put it forward as true.
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- Also stated in: § 1 INTRODUCTION · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“especially benefiting non-regular writers and less tech-savvy users.”
- Also stated in: § 1 INTRODUCTION · plain
“These benefits were particularly pronounced among non-regular writers and those less familiar with advanced technology, supporting the notion that adaptive writing tools are especially advantageous for these groups.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > Abstract
plaindirectn = 131- Asserted: passes — The authors put it forward as true.
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- Also stated in: § 1 INTRODUCTION · plain
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In the paper
“Remarkably, no significant changes were observed in the cognitive loads among the three conditions, as measured by the NASA load index.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 5.2 How did the "Emotional & Cognitive
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The loss of ownership was more pronounced in the paragraph condition, at -5.65 (p<0.0001),compared to -3.65 (p<0.0001) in the sentence condition.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 5.2 How did the "Emotional & Cognitive
plaincorrelational- Asserted: passes — The authors put it forward as true.
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In the paper
“Initial exposure to conditions seems crucial, with the highest text quality observed in the 'first condition, ' indicating participants' attentiveness impacts their output.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 5.5 Did the Position in the Sequence or the
hedged- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The sequence position control adequately accounts for order effects.
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In the paper
“The quality of writing in the sentence-level suggestion mode deteriorates compared to the baseline, indicated by a decline of -0.29 quality points (p=0.02).”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 5.1 How did the "Output Quality" change with scaffolding?
hedgeddirect- Asserted: passes — The authors put it forward as true.
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In the paper
“In contrast, AI interventions had no statistically significant impact on the writing quality of professional writers [10].”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 5.4 Did the impact of AI scaffolding vary with
What the sources say
- Not checked yetcites [10] 10. Daniel Buschek, Martin Zürn, and Malin Eiband. 2021. The impact of multiple parallel phrase suggestions on email input and composition behaviour of native and non-native english writers. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems. 1-13. source
plain- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The participants' self-reported writing expertise and technology usage categories accurately reflect their actual proficiency.
- It may state two separate claims as one. 22
- Probably not a substantial claim. 21
- The quote only partly states this claim. 1
A result the paper reports from its own data — 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 low scaffolding did not significantly improve writing quality or productivity, high scaffolding led to significant improvements, especially benefiting non-regular writers and less tech-savvy users.”
- Also stated in: § 1 INTRODUCTION · plain
“Conversely, high-level scaffolding-exemplified by nextparagraph suggestions-resulted in marked improvements in writing quality and productivity.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > Abstract
plaindirectn = 131- Asserted: passes — The authors put it forward as true.
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- Also stated in: § 1 INTRODUCTION · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“However, paragraph-level AI positively affected users with no technology expertise (increase of 0.55, p=0.02)”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 5.4 Did the impact of AI scaffolding vary with
plaindirect- Asserted: passes — The authors put it forward as true.
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- Rests on an assumption: The participants' self-reported writing expertise and technology usage categories accurately reflect their actual proficiency.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To minimize interference among participants, each individual was assigned to a separate breakout room to complete the task.Additionally, participants were required to activate their cameras and share their screens, a measure implemented to confirm that they were completing the tasks independently.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 3.1.2 Main Experiment:
plaindirect- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Participants were randomly assigned to one of three ordered sequences (37 in sequence 1, 53 in sequence 2, 41 in sequence 3), each experiencing all conditions with unique prompts to prevent response learning, thereby enhancing study validity.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > Levels of Scaffolding (Treatment Conditions):
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“we employ a withinsubjects field experiment designed with a Latin square arrangement (to mitigate order effects) [59].”
- Also stated in: § Levels of Scaffolding (Treatment Conditions): · plain
“Our study utilized a within-subjects design to efficiently examine AI scaffolding effects, employing a Latin Square design [59] to evenly distribute scaffolding conditions and minimize order biases.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 1 INTRODUCTION
What the sources say
- Not checked yetcites [59] 59. John TE Richardson. 2018. The use of Latin-square designs in educational and psychological research. Educational Research Review 24 (2018), 84-97. source
plaindirect- Asserted: passes — The authors put it forward as true.
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- Also stated in: § Levels of Scaffolding (Treatment Conditions): · plain
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“We acknowledge the inherent challenge in recruiting participants with neutral or negative views towards AI within our research context.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 3.4 Data Collection and Measurements
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
In the paper
“The foundational theory of scaffolding, introduced by Wood et al. [71], posits that such support enables learners to acquire skills and knowledge that would otherwise be difficult to attain independently.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 2.2 Scaffolding Strategies in Education Research
What the sources say
- Not checked yetcites Wood et al. [71]
plaindirect- Asserted: passes — The authors put it forward as true.
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What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“However, it is important to note that the survey question on AI attitudes was not a criterion for participant selection, nor was it used in our analyses.This aspect of the survey served solely as additional context and did not influence the outcomes of the study.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 3.4 Data Collection and Measurements
plain- Asserted: passes — The authors put it forward as true.
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Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To this end, we conducted over 100 hours of live Zoom sessions, allowing us to closely observe participants during the writing tasks.This rigorous monitoring approach was instrumental in providing reliable, high-quality data and was essential to maintaining the internal validity of our study.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 3.3 Participant Recruitment
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“This group, encompassing around 50,000 individuals, represented various socio-economic, ethnic, and professional backgrounds, significantly reducing the risk of selection bias.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 3.3 Participant Recruitment
boosteddirectn = 50,000- Asserted: passes — The authors put it forward as true.
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In the paper
“This design ensures that each participant is exposed to every treatment condition, thereby enhancing the internal validity of the study.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 1 INTRODUCTION
plaindirect- Asserted: passes — The authors put it forward as true.
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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
“A prominent finding is the U-shaped relationship between the level of scaffolding and writing quality/productivity-while low scaffolding adversely affected these metrics, high scaffolding conferred significant benefits.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 6.1 Summary of Key Results and Potential Mechanisms
plaindirectn = 393 ·- Asserted: passes — The authors put it forward as true.
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In the paper
“To mitigate this risk, we adjusted our treatment p-value estimates by applying a False Discovery Rate (FDR) correction [6].”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 4.2.1
What the sources say
- Not checked yetcites [6] 6. Yoav Benjamini and Yosef Hochberg. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological) 57, 1 (1995), 289-300. source
plain- Asserted: passes — The authors put it forward as true.
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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
“Less frequent writers benefit from more AI help, whereas experienced writers need less.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 6.2 Implications for Human-AI Co-writing
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“Many selected participants were unable to complete our main study due to scheduling conflicts.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > D GPT PARAMETERS
plaindirect- Asserted: passes — The authors put it forward as true.
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In the paper
“The research received approval from the Institutional Review Board affiliated with the first author's institution (HUM# 00221115).”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > D GPT PARAMETERS
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Compensation was appropriately calibrated to acknowledge participants' time without exerting undue influence.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 6.4 Ethical Considerations
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“All participants provided informed consent, and identifiers were anonymized to protect privacy.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 6.4 Ethical Considerations
plain- Asserted: passes — The authors put it forward as true.
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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
“Fourth, only short-term effects were studied in brief, one-time sessions.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 6.3 Limitations and Generalizability
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“Third, the sample primarily comprised English speakers proficient in writing.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 6.3 Limitations and Generalizability
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“This choice is grounded in the fundamental structure of language writing where sentences form the basic building blocks of coherent thought, and paragraphs represent larger units of ideas or arguments [43].”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 1 INTRODUCTION
What the sources say
- Not checked yetcites [43] 43. Andrea A Lunsford and John J Ruszkiewicz. 2016. Everything's an Argument. Bedford/St. Martin's.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“This could suggest that professional writers already possess wellhoned skills that AI cannot readily augment [27].”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 5.4 Did the impact of AI scaffolding vary with
What the sources say
- Not checked yetcites [27] 27. Katy Ilonka Gero and Lydia B Chilton. 2019. How a Stylistic, Machine-Generated Thesaurus Impacts a Writer's Process. In Proceedings of the 2019 on Creativity and Cognition. 597-603. source
hedgedconditional- Asserted: passes — The authors put it forward as true.
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In the paper
“We chose a relatively low threshold score of 2 as we aimed to investigate how the degree of AI assistance in writing would impact individuals across a spectrum of writing skills, including both proficient and less proficient writers.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 3.1.1 Pre-screening task:
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“We correct for multiple comparisons via False Discovery Rate (FDR)[6].”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > D GPT PARAMETERS
What the sources say
- Not checked yetcites [6] 6. Yoav Benjamini and Yosef Hochberg. 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal statistical society: series B (Methodological) 57, 1 (1995), 289-300. source
plainn = 393- Asserted: passes — The authors put it forward as true.
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In the paper
“Participants' responses were scored on a 1-5 scale by two evaluators using the TOEFL rubric (Appendix A.2) [61].”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 3.1.1 Pre-screening task:
What the sources say
- Not checked yetcites [61] 61. Educational Testing Service. 2023. TOEFL iBT Writing Rubrics. https://www.ets. org/pdfs/toefl/toefl-ibt-writing-rubrics.pdf Accessed: 2023-09-06.
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“These prompts were chosen to offer a range of accessible and balanced topics, aligning well with our research goals.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > Writing Prompts:
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“The study underwent review by an institutional ethics board to ensure protections were in place.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 6.4 Ethical Considerations
plain- Asserted: passes — The authors put it forward as true.
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In the paper
“This procedural setup was designed to create a controlled yet flexible environment for capturing reliable participant writing data.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 3.1.2 Main Experiment:
plain- Asserted: passes — The authors put it forward as true.
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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
“First, the experiment examined only two discrete levels of scaffolding-sentence and paragraph suggestions.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 6.3 Limitations and Generalizability
plain- Asserted: passes — The authors put it forward as true.
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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 might suggest that while paragraph-level suggestions provide enough content to boost writing speed, sentence-level suggestions may not offer sufficient guidance to make a noticeable difference in productivity.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 5.3 How did the "Task Efficiency & Persuasion"
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.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Since we have multiple observations per subject, we cluster the standard errors at the level of users, to control for serial correlation of errors.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 4.2.1
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.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“To ensure the integrity of our main experiment, we held multiple live virtual writing sessions through Zoom, each accommodating between 1 and 10 participants.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 3.1.2 Main Experiment:
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.
In the paper
“Conversely, non-experts or those experiencing writer's block might find initial AI suggestions invaluable to kick-start their writing process, editing and refining AI-generated content to better align it with their own voice and objectives [32].”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 1 INTRODUCTION
What the sources say
- Not checked yetcites [32] 32. Iona Gilburt. 2023. A machine in the loop: the peculiar intervention of artificial intelligence in writer's block. New Writing 0, 0 (2023), 1-12. https://doi.org/10. 1080/14790726.2023.2223176 arXiv:https://doi.org/10.1080/14790726.2023.2223176 source
hedgedconditional- 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
“The decline in writing quality after removing AI scaffolding suggests an over-reliance on AI support, reflecting the 'learned helplessness' concept in psychology [44]”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 6.1 Summary of Key Results and Potential Mechanisms
What the sources say
- Not checked yetcites [44] 44. Steven F Maier and Martin EP Seligman. 2016. Learned helplessness at fifty: Insights from neuroscience. Psychological review 123, 4 (2016), 349.
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.
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
“Second, the prompts covered only argumentative writing in a standardized test format.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 6.3 Limitations and Generalizability
plain- 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: The 10 argumentative writing prompts are comparable in difficulty and representative of argumentative writing.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Additionally, the nature of our study necessitated meticulous monitoring of participant engagement to ensure data integrity and prevent the use of AI tools, like Chat-GPT, in their responses-a level of oversight that would have been challenging to achieve with remote participants on platforms like Mechanical Turk or Prolific.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 3.3 Participant Recruitment
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
“On the other end of the spectrum, excessive AI scaffolding, such as full-paragraph generation, carries its own set of challenges.While it may expedite the writing process, it can also deter the writer from critically engaging with the subject matter, reducing the depth of their understanding and their emotional investment in the project [16,72].”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 1 INTRODUCTION
What the sources say
- Not checked yetcites [16] 16. Ruijia Cheng, Alison Smith-Renner, Ke Zhang, Joel R Tetreault, and Alejandro Jaimes. 2022. Mapping the design space of human-ai interaction in text summa- rization. arXiv preprint arXiv:2206.14863 (2022).
- Not checked yetcites [72] 72. David Wood, Jerome S Bruner, and Gail Ross. 1976. The role of tutoring in problem solving. Journal of child psychology and psychiatry 17, 2 (1976), 89-100.
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
“Participants completed a survey to self-evaluate their perceived cognitive and emotional workload after completing each writing task, utilizing the NASA Task Load Index (NASA-TLX) [34] (described in Table 1).”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 3.4 Data Collection and Measurements
What the sources say
- Not matchedcites [34] 34. Sandra G. Hart and Lowell E. Staveland. 1988. Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research. In Human Mental Workload, Peter A. Hancock and Najmedin Meshkati (Eds.). Advances in Psychology, Vol. 52. North-Holland, 139-183. https://doi.org/10.1016/S0166- 4115(08)62386-9
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
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.
- Not matched
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“High-level scaffolding can provide a more structured framework, akin to giving carpenters a blueprint to follow, thereby reducing the cognitive load and making the writing task more manageable.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 1 INTRODUCTION
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.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“This approach standardized the conditions under which participants completed their tasks, facilitating a fair comparison of their abilities.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 3.1.1 Pre-screening task:
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.
The paper relies on this without support.
Current research provides limited insight into how different intensities of AI input affect user experience and writing outcomes.
“Hence, our work responds to a gap in current research: while the use of AI in writing is well-documented, there's limited insight into how varying intensities of AI input influence user experience and writing outcomes.”
The paper relies on this without support.
The two human raters provided reliable and consistent quality assessments.
“we incorporated a third metric based on independent human evaluations by two independent raters, assessing the writing based on its response to the prompt, the conciseness of the answer, and the quality of argumentation.”
The paper relies on this without support.
The 10 argumentative writing prompts are comparable in difficulty and representative of argumentative writing.
“In our study, we employed a set of 10 argumentative writing prompts, carefully chosen to cover a wide range of subjects.”
The paper relies on this without support.
The participants' self-reported writing expertise and technology usage categories accurately reflect their actual proficiency.
“𝑒𝑥𝑝𝑒𝑟𝑡𝑖𝑠𝑒 𝑖 codes the writing proficiency (not regular, regular, and proficient) of the user and 𝑡𝑒𝑐ℎ_𝑢𝑠𝑒 𝑖 represents their tech-savviness (no technology use, basic use, and advanced use).”
The paper relies on this without support.
The AI-generated suggestions are of sufficient quality and relevance to serve as scaffolding.
“We used GPT-3 davinci as the backend Large Language Model (LLM) for our experiments since it was the state-of-the-art when we performed this study.”
The paper relies on this without support.
The sequence position control adequately accounts for order effects.
“We also control for 𝑝𝑜𝑠𝑖𝑡𝑖𝑜𝑛 𝑖 𝑗 , which indicates the sequence position at which a specific condition was encountered by a participant.”
The paper relies on this without support.
The number of edits is a valid proxy for user effort.
“Number of Edits: The total number of edits (additions, deletions, changes) made by the participant.”
The paper relies on this without support.
The count of spelling errors is a valid measure of error types in the writing task.
“Number of Errors: The total number of spelling errors in the text output.”
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
“Our synthesis shows systems instantiating varied distributed agency arrangements [68], from human-centric [45] to shared partnerships [206].”
From Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature (2025) · cites this paper as [45]
The cited source says what the claim attributes to it.
In the citing paper
“and that suggesting whole paragraphs of text may increase writing quality but at the cost of decreased feelings of ownership [17].”
From Where Do I 'Add the Egg'?: Exploring Agency and Ownership in AI Creative Co-Writing Systems (2026) · cites this paper as [17]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Agency dynamics in news curation typically involve semi-proactive and reactive mechanisms.”
From Exploring Collaboration Patterns and Strategies in Human-AI Co-creation through the Lens of Agency: A Scoping Review of the Top-tier HCI Literature (2025) · cites this paper as [45]
Experimental — accuracy not yet measured
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 major: it would touch a result — a value a result rests on, reported and recomputed p-values on opposite sides of the significance level, two passages that conflict with no sign which is right, or a cited work retracted before this paper was published.
In the paper
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 5.4 Did the impact of AI scaffolding vary with
“The AI conditions slightly increased the number of errors made by both regular and professional writers (around 3 more errors) compared to the baseline no AI assistance condition (Table 5).”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > D GPT PARAMETERS
“| 𝑒𝑥𝑝𝑒𝑟𝑡𝑖𝑠𝑒 𝑖 : Professional × 𝑐𝑜𝑛𝑑𝑖𝑡𝑖𝑜𝑛 𝑗 : Paragraph | -2.48 (4.52) | -0.65 (0.54) | | | |”
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
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 3.3 Participant Recruitment
“Post-screening, we selected participants who put in some "effort" in the writing task, as evidenced by scoring atleast 2 out of 7 from two independent raters.”
§ Shaping Human-AI Collaboration: Varied Scaffolding Levels in Co-writing with Language Models > 3.1.1 Pre-screening task:
“Participants' responses were scored on a 1-5 scale by two evaluators using the TOEFL rubric (Appendix A.2) [61].”
Citations
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Fujiwara, M., Chujo, R., & Hautasaari, A. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719718 · Cited by 1
Kristianto, V. A., Sucipto, S., Sri, H., & Yuhanafia, N. (2025) · Language Circle Journal of Language and Literature · doi:10.15294/lc.v19i2.21540 · Cited by 1
SYNthia: An Interface Concept for Writing With Large Language Models
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Human-AI Collaborated Ideation for Reduce & Reuse in Waste Management
Sun, Q., & Hsiao, I. (2025) · ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) · doi:10.1145/3715335.3736322 · Cited by 1
Sirita, U., & Todd, R. W. (2025) · rEFLections · doi:10.61508/refl.v32i3.285887 · Cited by 2
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Jin, H., Li, Y., Jiang, Y., & Dong, C. (2026) · Computer Assisted Language Learning · doi:10.1080/09588221.2026.2628044 · Cited by 1
Nayak, A. K., Prakash, Y., Jayarathna, S., Lee, H.-N., & Ashok, V. G. (2026) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791509 · Cited by 2
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From Planning to Revision: How AI Writing Support at Different Stages Alters Ownership
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“For example, previous studies on human–AI co-creation in fields such as music composition [51, 72] and creative writing [28, 38] have demonstrated how generative AI systems, such as large language models and image generation models, can accelerate brainstorming, lower barriers to entry, and enable…”
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Metadata
- Authors
- Dhillon, P. S., Molaei, S., Li, J., Golub, M., Zheng, S., & Robert, L. P.
- Affiliations
- University of Michigan (US)
- University of California San Diego (US)
- ORCID
- Venue
- Proceedings of the CHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2024-05-11
- Pages
- 1-18
- OpenAlex
- W4396833054
- Cited by
- 111
- References
- 50
- Citations by year
- 2026: 60 · 2025: 48 · 2024: 3
- FWCI
- 35.65
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- CHI Conference on Human Factors in Computing Systems (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Human-Computer Interaction › Usability and User Interface Design
- Social Sciences › Human Factors and Ergonomics › Human-Automation Interaction and Safety
- Physical Sciences › Computer Science › Human-Computer Interaction › Innovative Human-Technology Interaction
- Keywords
- large language models, scaffolding, argumentative writing, writing quality, writing productivity, user satisfaction, cognitive burden, AI writing tools
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 2026-10-01, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-01 (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 ›
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