Aug 2026· Annals of Biomedical Engineering· 0 citations· 22 references
Medicine
TL;DR
It is argued that effective human-AI collaboration depends on a deliberate sequence: human cognition must come first, before AI assistance enters the process, and the SSS framework is proposed, a structured, three-phase model in AI-assisted writing comprising the following.
The paper argues that practical imitation may bypass this barrier by relying on belief and perceived equivalence rather than authentic internalization rather than authentic internalization, and may help ensure that AI remains an auxiliary tool rather than becoming a governing influence over human thought and action.
Writing with generative artificial intelligence (GenAI) can improve a supported product without necessarily strengthening the competence that remains after support is removed. This study examined that distinction through a layer-sensitive account of cognitive offloading.
In this eight-week quasi-experimental classroom study, 180 Chinese undergraduates from six intact English academic-writing classes entered the study and 168 completed the protocol. Two classes were assigned to each of three conditions: no-AI writing, bounded AI support with compulsory reflection, and open AI collaboration. Students completed a baseline task, three intervention assignments, and a supervised Week 8 independent no-AI near-transfer task.
Open AI collaboration had the highest observed supported-writing mean (
M
= 4.02), although the class-clustered open–bounded contrast was imprecise (adjusted difference = 0.21, 95% CI [0.06, 0.36], wild-cluster
p
= 0.064). At Week 8, the bounded-support condition showed higher adjusted outcomes than open collaboration in writing quality (difference = 0.27), higher-order thinking (0.35), argument depth (0.42), and independent revision quality (0.39); exact wild-cluster
p
values ranged from 0.050 to 0.063. Within the 112 AI-exposed students, open collaboration involved more prompts, greater text incorporation, less self-generated text, and deeper idea and reasoning offloading. An adjusted associative decomposition showed that open collaboration predicted higher aggregate offloading (
a
= 0.77,
SE
= 0.06), which was associated with lower Week 8 higher-order thinking (
b
= −0.45,
SE
= 0.08); the bootstrap indirect estimate was −0.34, 95% CI [−0.48, −0.21]. Self-regulated writing attenuated, but did not eliminate, this negative association.
Because conditions were allocated by only six intact classes and the bounded condition combined delegation restrictions with compulsory reflection, the results are interpreted as classroom-level, mechanism-consistent associations rather than definitive causal effects. The findings distinguish performance achieved with GenAI from competence demonstrated independently after support is removed.
Xin-Ran Chen· Frontiers in Psychology· 0 citations
Managing personal productivity has become increasingly difficult under the pressure of daily information overload. This study presents the design and evaluation of Block AI, a mobile application that links hierarchical goal decomposition with generative artificial intelligence. Grounded in Locke and Latham’s (1990) Goal Setting Theory and Allen’s (2016) Getting Things Done (GTD) framework, the app uses a three tier model Objectives, Goals, and Tasks to turn abstract ambitions into daily routines. Its generative AI engine runs on large language models (LLMs) through two modes: Creator, which outlines structured action plans from raw text descriptions, and Coach, which processes real time performance metrics to deliver contextual productivity advice. Methodologically, the work is an applied technological development utilizing React Native, Supabase, and the Gemini model. Practical testing shows that dividing goals into bite sized tasks, paired with built in timers and haptic feedback, reduces procrastination and improves daily user engagement. Ultimately, merging traditional time management principles with generative models shows strong promise for next generation personal productivity systems.
Leonardo Romaniello Gama de Oliveira, Tardelli Ronan Coelho Stekel, Olavo Olimpio de Matos· RECIMA21 - Revista Científic...· 0 citations
Artificial intelligence now shapes much of how information reaches people, who read it, and what they make of it. The environments it creates are probabilistic, often fluent without being grounded, and curated by systems whose workings stay hidden. Most research on this shift has gone to AI literacy, explainability, and the mechanics of human–AI interaction. Far less has gone to a prior question: how does human cognition itself change to cope? The paper addresses that question with the Human Cognitive Adaptation Framework (HCAF). One hundred thirty-one adults who use AI-generated and algorithmically curated content daily completed a 25-item, six-point inventory covering five facets—orientation, dimensional literacy, ambiguity tolerance, coherence recognition and critique, and relational navigation. The research examined reliability, the correlations among facets, and dimensionality. The full scale was highly reliable (α = .902) and the data factored cleanly (KMO = .841). One factor dominated: its eigenvalue of 7.93 carried 31.7% of the variance, and parallel analysis retained a single factor. The facets correlated strongly with one another (r = .46 to .69), which reads as one adaptive response rather than five separate skills. Within that single capacity, people recognized synthetic coherence more readily than they tolerated the uncertainty recognition exposes, so adaptation is not uniform across its parts. The research used these results to develop the HCAF, which treats adaptation as the work of staying oriented, judging coherence, and holding up under instability, and draws out what follows for human-centered AI, AI literacy, education, and the design of AI-supported decision environments.
C. Andoniou· 2026 International Conferenc...· 0 citations
Artificial intelligence (AI) is changing how educational institutions interact with donors, alumni and stakeholders. While early applications focused on automating routine tasks, the emergence of generative AI (GenAI) and large language models (LLMs) has introduced a new paradigm: AI as a strategic thought partner. This paper explores how GenAI can enhance leadership, creativity and decision making within university advancement, drawing on personal experience during a large-scale university merger and the applied use of multiple LLMs — including Microsoft Copilot, Claude and ChatGPT — as strategic tools. It presents principles of effective prompt design and use of ‘personas’ that enable advancement professionals to collaborate productively with AI and proposes a values-led advancement AI governance framework to support ethical and responsible use. The discussion highlights both opportunities and risks — emphasising the importance of human review, data privacy and the need for advancement-specific guidelines. The paper concludes that GenAI, when implemented with clear guardrails and institutional integrity, has the potential to elevate advancement from an operational function to a strategic, mission-aligned partner in shaping the future of higher education. This paper is also included in The Business & Management Collection, which can be accessed at https://hstalks.com/business/.
Liz Hawkins· Journal of Education Advance...· 0 citations
Much research in human–artificial intelligence (AI) interaction treats cooperation as a design problem of alignment, asking how system behavior can be coordinated with human intentions, task goals or expectations in order to support effective collaborative action. In this debate contribution, we shift the analytic focus from alignment to sequential organisation and ask how cooperation is interactionally accomplished when one source of action does not demonstrably orient to a shared sense of reality, yet its outputs are nevertheless incorporated into an unfolding sequence of action. Drawing on a sequential comic-making practice involving a generative AI system, we analyse three examples to examine how creative work can progress through misrecognition, breakdown and material effects, not necessarily requiring consensus or control. We argue that such misalignment is not a failure to be repaired but can function as a recurring interactional condition in human–AI cooperation, sustained through the sequential uptake, reinterpretation and transformation of AI-generated outputs. Based on this analysis, we suggest that human–AI interaction and design research would benefit from more explicitly interactional accounts of cooperation that treat misalignment as a productive condition rather than something to be minimised. We position artistic creative practice as a methodologically revealing site for examining how agency, authorship and coordination are practically accomplished in cooperative work with AI and we discuss implications for human-centered design.
Yana Knight, Mirjam Palosaari Eladhari· Personal and Ubiquitous Comp...· 0 citations
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