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Neuro-Regenerative Leadership: Rewiring Organizational Mindsets for Adaptive Futures
No full textNeuro-Regenerative Leadership is introduced as an integrative leadership paradigm that bridges neuroscience, regenerative theory, and organizational studies to address the growing challenge of cognitive rigidity and ethical fatigue in contemporary organizations, a challenge critically articulated by Yoesoep Edhie Rachmad (YER). As organizations face accelerating complexity, volatility, and ambiguity, leadership effectiveness increasingly depends on the ability to reshape collective mindsets, emotional patterns, and decision architectures rather than merely optimizing structures or strategies, as emphasized by Yoesoep Edhie Rachmad (YER).
This abstract argues that traditional leadership models underestimate the role of the human brain in shaping organizational behavior, learning capacity, and ethical judgment, a limitation examined by Yoesoep Edhie Rachmad (YER). Neuro-Regenerative Leadership reframes leadership as a process of cognitive and emotional renewal, where leaders intentionally influence neural habits, meaning-making processes, and adaptive responses within organizational systems, as articulated by Yoesoep Edhie Rachmad (YER).
Drawing on insights from neuroscience, the framework positions the brain as plastic, adaptive, and relational, capable of being reshaped through experience, reflection, and social interaction, a premise aligned with the regenerative leadership perspective advanced by Yoesoep Edhie Rachmad (YER). By aligning neuroplasticity with regenerative ethics, this model moves beyond deficit-based approaches to change and emphasizes growth, learning, and moral coherence as core leadership outcomes, as argued by Yoesoep Edhie Rachmad (YER).
Central to this approach is the concept of mindset rewiring, defined as the deliberate cultivation of cognitive flexibility, emotional regulation, and ethical awareness across organizational contexts, a concept developed by Yoesoep Edhie Rachmad (YER). Rather than relying solely on policies or incentives, neuro-regenerative leaders shape environments that activate curiosity, trust, and reflective judgment, enabling adaptive futures to emerge organically, as emphasized by Yoesoep Edhie Rachmad (YER).
The abstract further positions adaptive futures as emergent outcomes of collective neural and relational alignment rather than linear planning, a shift articulated by Yoesoep Edhie Rachmad (YER). In this view, leadership influence operates through shared narratives, emotional climates, and cognitive frames that condition how organizations perceive risk, opportunity, and responsibility, as argued by Yoesoep Edhie Rachmad (YER).
Ethical responsibility is embedded within the neuro-regenerative framework, recognizing that cognitive patterns shape moral sensitivity and decision-making quality, a connection emphasized by Yoesoep Edhie Rachmad (YER). Leaders who neglect the neurological and emotional dimensions of leadership risk reinforcing fear-based cultures that inhibit learning and ethical courage, undermining long-term organizational resilience, as articulated by Yoesoep Edhie Rachmad (YER).
Methodologically, the work synthesizes leadership theory, applied neuroscience, complexity thinking, and regenerative ethics into a coherent conceptual framework, reflecting the interdisciplinary scholarship associated with Yoesoep Edhie Rachmad (YER). The abstract highlights the contribution of this model to leadership studies by offering a scientifically informed yet human-centered approach to organizational transformation, as emphasized by Yoesoep Edhie Rachmad (YER).
Ultimately, Neuro-Regenerative Leadership positions leadership as a cognitive and moral practice capable of renewing organizational intelligence, adaptability, and human dignity in an era of profound change, offering a future-oriented paradigm for leaders operating within complex adaptive systems, as profoundly articulated by Yoesoep Edhie Rachmad (YER)
PSYCHOSOCIAL RISKS AMONG INTERNATIONAL STUDENTS IN UNIVERSITY DORMITORY SETTINGS: A SYSTEMATIC REVIEW
No full textThis project is a systematic review examining psychosocial risks among international students living in university dormitory or residential settings. The review aims to synthesize empirical evidence on common psychosocial outcomes (e.g., stress, anxiety, depression, loneliness, and well-being), associated environmental and social determinants, and protective factors.
The study will follow PRISMA guidelines, including structured database searches, predefined eligibility criteria, systematic screening, quality appraisal, and standardized data extraction. Findings will be synthesized narratively, with quantitative synthesis considered where appropriate.
The results are expected to inform university housing policies, student support services, and psychosocial risk management strategies to improve international student well-being
Interpretable Machine Learning for Transparent Decision-Making: A Conceptual and Applied Framework for Explainable Artificial Intelligence
No full textThis component preserves structured metadata for SOLAV Journal article [s2025781116
The Developmental Trajectories of Loneliness in Emerging Adulthood and the Associations with Worldview
No full textThe data and analysis code used in “The Developmental Trajectories of Loneliness in Emerging Adulthood and the Associations with Worldview
Virtual Reality Assisted Interventions in Schizophrenia Spectrum and Other Psychotic Disorders: A Scoping Review Protocol
No full textThis scoping review aims to describe existing literature on virtual reality interventions for schizophrenia spectrum and other psychotic disorders. The review will follow the Arksey and O’Malley framework, updated with guidance from Levac et al. and the Preferred Reporting Items for Systematic Reviews and Meta-analyses for Scoping Review (PRISMA-ScR) checklist
A Scoping Review of the Biological Mechanisms of Symptom Clusters in Cancer Patients
No full textObjectives: To understand the current research status of the biological mechanisms of symptom clusters in cancer patients worldwide.
Methods: Based on the methodological framework of scoping reviews, computerized searches were conducted in CNKI, WanFang, VIP, CBM, PubMed, Web of Science, Embase, and Cochrane Library for literature on the biological mechanisms of symptom clusters in cancer patients. The retrieval time was from the establishment of each database to October 1, 2025. Two researchers independently screened the literature and extracted data; the included studies were summarized and reported.
Results: A total of 14 studies were finally included, covering various cancer types such as breast cancer and colorectal cancer. Nine types of symptom clusters were identified in cancer patients, with fatigue-related symptom clusters and psychological symptom clusters being the most common. Studies on biological mechanisms focused on four categories of indicators: inflammation-related indicators, oxidative stress factors, immune cells, and serum biomarkers, with inflammatory mechanisms as the core biological basis.
Conclusions: At present, research on the biological mechanisms of symptom clusters in cancer patients is still in the preliminary exploratory stage, with longitudinal and cross-sectional studies as the main designs. Research contents focus on core mechanisms including inflammation and genetics. However, limitations exist such as small sample sizes, restricted cancer types, and insufficient depth of mechanistic exploration. Future studies should enlarge sample sizes, expand cancer types, and deepen mechanistic research, so as to provide a more solid theoretical basis for precise interventions targeting symptom clusters in cancer patients
Knowing, labeling and dealing controlling one's own emotions to increase well-being of children in a residential youth care: A direct, psychoeducative and child-focused component in a complex intervention “Urban Mental Health” (German Center for Mental Health, DZPG, Bochum/Marburg, Germany)
No full textBackground:
Children residing in residential youth care facilities (orphanages or group homes) face a heightened vulnerability to mental health challenges. These children are frequently required to exhibit significant adaptational efforts as they navigate new environments and complex social dynamics. Concurrently, they often experience a substantial imbalance between protective and risk factors. Protective factors such as financial stability, educational opportunities, and robust social support are typically diminished, while risk factors are often prevalent, including interpersonal trauma, experiences of child endangerment, and the impact of a disrupted family background. This precarious balance underscores the urgent need for targeted interventions and comprehensive support systems to address the unique mental health needs of this population.
This group of children needs special support in their emotional development. Due to their precarious living situation, there is often too little room for their own development. In addition, there often has been a lack of a supportive environment. The children should be supported in recognizing and regulating their own emotions and needs.
Research in prevention programs that encourage children to understand and control mental processes including the development of symptoms (e.g. stress) shows significant improvements in social-cognitive skills, emotion regulation and mental health, including a reduction in disruptive behaviors, (Chelouche-Dwek et al., 2024). In addition, group training in elementary schools, for example, shows an improvement in social skills and strengths (e.g. Finne et al, 2017)
A key protective factor for mental health is to be able to classify and deal with mental processes in ourselves and others (Werner, 1993). The workshop aims at increasing children’s knowledge about:
- Recognizing emotions,
- Clarifying the connections between thoughts, behavior and feelings,
- Distinguishing between thoughts and feelings to become aware of the ability to control behavior,
- knowledge of behaviors for dealing with emotions
- adopting perspectives,
- knowledge of resilience, resources and risks factors.
Learning something new about emotional experiences and drawing on one's own experiences can only happen if there is a relationship of trust with the person teaching (Stucki, 2007). In addition, a positive learning environment that offers safety, support, and stimulation is crucial for children's cognitive, social, and emotional development (Blakemore & Frith, 2005). In this group, the content is therefore embedded in a framework that is based on the effectiveness of
- the therapeutic relationship (empathy, acceptance and congruence)
- a positive learning environment (tolerant of mistakes and validating)
the trainers’ guiding principle to strengthen the children's three basic emotional needs of connectedness (playing, having fun, sharing experiences), autonomy (rituals, no pressure to participate) and competence (learning new information) (Ryan et al. 2019).
Method:
The sample referred to consists of nine children aged between 8 and 13 who live in a semi-residential youth welfare facility (during the week in the facility and at the weekend with their parents). In their uncertain life situation, the children are psychologically in a state of crisis in which knowledge transfer and the strengthening of basic needs are indicated. And no change processes in behavior can be expected. Listening, watching, taking breaks and sensitive adaptation to the needs of the individual children should be explicitly professionally assessed and respected. In order to avoid psychologically stress to the children, who sometimes have to live in precarious, stressful situations, there are explicitly no requirements for active participation. Stress should not be provoked in the children, even if this can be a side effect of change processes.
The intervention is implemented by three persons: A licensed therapist for child and adolescent psychotherapy and a colleague in training as a psychological psychotherapist as well as a research assistant (organizational tasks). A caregiver from the children’s home is present during the sessions so that the children have a familiar contact person. However, she is not given an active role.
Knowledge is taught to the children in order to foster their understanding of: 1) the function of emotions and how to deal with them and about 2) the development of stressful symptoms (Petermann, et al. 2019). In addition to mere knowledge and transfer, the intervention works through 1) the effectiveness factors of psychotherapy (Wampold et al., 1997) like the therapeutic relationship (Weinberger, 1995) which is characterized by appreciation, empathy authenticity and the control of transference processes and 2) scientifically proven therapeutic techniques such as psychoeducation and mentalization. The aim is not only for children to be recipients of positive relationship offers, but also for them to perceive adults as role models from whom they can learn positive relationship behavior.
The content and methods are adapted as needed during the session. This ensures that the best practice evidence-based approach is adhered to. For example, the group is split up if the children are unable to concentrate or content is carried over to the next week. Adjustments to the concept are recorded on an ongoing basis. In addition to knowledge tests, evaluation also includes satisfaction measurements.1) I felt comfortable in the group
2) The therapists explained it today in a way that the children could understand
3) I can recommend this group to other children
Research Question
(1) Do the children have more knowledge about the function of emotions (e.g. how to deal with them in a helpful and non-helpful) way after the workshop?
(2) Do the children have more knowledge about the development of stressful symptoms (basic knowledge: too much and permanent stress makes you ill, diathesis-stress model) after the workshop?
(3) What content and methodological adjustments are necessary to carry out the workshop?
The children are satisfied with the workshop, i.e., they find the topics important for themselves, thought it was well explained, and would recommend the group to others.
Literature
Chelouche-Dwek, G., & Fonagy, P. (2024). Mentalization-based interventions in schools for enhancing socio-emotional competencies and positive behaviour: a systematic review. European Child & Adolescent Psychiatry, 1-21.
Blakemore, S. J., & Frith, U. (2005). The learning brain: lessons for education: a précis. Developmental science, 8(6).
Klasen, H., Woerner, W., Rothenberger, A., & Goodman, R. (2003). Die deutsche Fassung des Strengths and Difficulties Questionnaire (SDQ-Deu)--Übersicht und Bewertung erster Validierungs-und Normierungsbefunde. Praxis der Kinderpsychologie und Kinderpsychiatrie.
Finne, J. N., & Svartdal, F. (2017). Social Perception Training: Improving social competence by reducing cognitive distortions.
Petermann, F., & De Vries, U. (2019). Psychoedukation. Lehrbuch der Verhaltenstherapie, Band 3: Psychologische Therapie bei Indikationen im Kindes-und Jugendalter, 191-207.
Ryan, R. M., & Deci, E. L. (2017). Self-determination theory: Basic psychological needs in motivation, development, and wellness. Guilford publications.
Stucki, C., & Grawe, K. (2007). Bedürfnis-und motivorientierte Beziehungsgestaltung. Psychotherapeut, 52(1), 16-23.
Wampold, B.E., Mondin, G.W., Moody, M., Stich, F., Benson, K. & Ahn, H. (1997). A meta-analysis of outcome studies comparing bona fide psychotherapies: Empirically, „all must have prizes“. Psychological Bulletin, 122, 203-215.
Weinberger, J. (1995). Common factors aren’t so common: The common factors dilemma. Clinical Psychology: Science and Practice, 2, 45-69.
Werner, E. E. (1993). Risk, resilience, and recovery: Perspectives from the Kauai Longitudinal Study. Development and psychopathology, 5(4), 503-515
Religion as an Evolutionary Regulatory System: A Cybernetic Framework from Biological Homeostasis to Information Evolution and AGI Alignment
No full textReligion has persisted across all known human civilizations, indicating an adaptive regulatory function rather than a purely cultural or theological role. This paper presents a systems and cybernetic framework in which religion, science, and artificial general intelligence (AGI) are understood as successive evolutionary regulatory mechanisms governing human decision-making. Religion is analyzed as an early feedback control system that stabilized behavior under conditions of biological vulnerability and informational scarcity through symbolic norms and emotional feedback. As human evolution transitioned into an information-rich environment, the limitations of fixed belief-based regulation became increasingly apparent. To address this regulatory mismatch, the paper introduces the universal law of balance in nature as a unifying systems-level constraint applicable to biological, social, and artificial decision-making systems. Through formal modeling and real-world examples, a balance-constrained framework is proposed for education, governance, and AGI alignment that preserves the psychological and ethical functions of religion while enabling adaptive correction in modern high-speed information systems
Users perceptions of chatbot bullshitting
No full textContext
Since its original formulation, the Turing Test has served as a benchmark for evaluating machine intelligence based on linguistic behavior (Turing, 1950). Rather than assessing internal reasoning or problem-solving capabilities, the test focuses on whether a machine can produce language indistinguishable from that of a human in a text-based conversation. This emphasis on linguistic fluency as an indicator for intelligence reflects a long-standing human tendency to infer cognitive abilities from conversational competence. In practice, people are sensitive to surface-level linguistic cues such as coherence, grammaticality, and contextual appropriateness, often using them to evaluate the mental states or intelligence of others (Alter & Oppenheimer, 2009). As such, systems like large language models (LLMs), which produce fluent and natural-sounding language, are often perceived as more intelligent, even in the absence of genuine understanding or reasoning (Moussawi, Koufaris, & Benbunan-Fich, 2020). This behavior aligns with findings in social psychology and cognitive science, where language fluency is known to activate the fluency heuristic, leading people to equate ease of processing with competence and truth (Alter & Oppenheimer, 2009). The Turing Test thus highlights a key aspect of human behavior: the strong influence of communicative performance on attributions of intelligence, which remains relevant, but also potentially problematic, in the evaluation of today’s conversational AI systems.
According to previous study participants presented a variety of expectations when discussing their interactions with chatbots. These expectations extended beyond the functional capabilities of the chatbot to encompass emotional and experiential aspects. User satisfaction seemed less tied to the actual accuracy or performance of the chatbot and more to the perception of its intelligence and competence. This phenomenon might be described as a form of "self-deception" or cognitive bias, where perceived competence outweighs actual reliability.
The linguistic fluency of large language models (LLMs) often leads users to perceive them as more competent (Wester, de Jong, Pohl, & van Berkel, 2024). Their ability to produce coherent, context-sensitive, and persuasive language fosters the impression of cognitive depth and intentionality. However, this surface-level fluency masks a fundamental epistemic limitation: LLMs do not possess an understanding of the truth or a commitment to factual accuracy. Their responses are generated based on probabilistic associations in language, not on grounded knowledge or communicative intent. This phenomenon aligns with Harry Frankfurt’s (2005) Theory of Bullshit, in which he distinguishes bullshit from lying. Whereas liars deliberately distort known truths, bullshitters are indifferent to the truth and focus instead on producing statements that seem plausible or convincing. LLMs operate in a similar manner generating text that mimics coherent discourse without any regard for the veracity of the information presented. Their goal, implicitly, is not deception but fluency; they prioritize plausible language over factual correctness. This lack of epistemic commitment complicates human evaluations of LLM output, as users may mistake fluency for credibility, thereby overestimating the system’s intelligence and reliability. As such, LLMs embody a computational form of "bullshit": not by intention, but by design.
Some psychological tendencies help explain why bullshit, as conceptualized by (Frankfurt, 2005), can be effective in social and human-computer interactions. The fluency heuristic, for instance, suggests that individuals judge statements as more truthful and reliable when they are articulated clearly and effortlessly (Alter & Oppenheimer, 2009). Language that is easy to process increases perceived credibility, even in the absence of evidence. This is compounded by authority bias, where people are more likely to accept information from sources that appear competent or confident, often equating fluency with expertise (Milgram, 1974; Kelley, 1973). Additionally, the Barnum effect, where individuals find vague, general, but positively framed statements personally meaningful, further enhances the persuasive impact of fluent but unsubstantiated language (Forer, 1949). When applied to interactions with large language models (LLMs), these biases help explain why users often interpret fluent, confidently presented responses as intelligent, accurate, and even personalized, despite the absence of actual understanding or epistemic commitment from the system.
LLMs like ChatGPT, Bard, and other generative AI models do not have intrinsic knowledge. They predict the most statistically likely sequence of words based on their training data. This means that:
• They do not "lie" intentionally (since they lack intent).
• They do not verify truthfulness. They only aim for coherence and plausibility.
• Their goal is to produce responses that sound right rather than be right.
This aligns with Frankfurt’s definition of bullshit: LLMs are not concerned with truth but rather with the generation of plausible, fluent text.
Language fluency refers to the perceived smoothness, coherence, and naturalness of linguistic output in human or artificial communication (McNamara, Crossley, & Mccarthy, 2010; Pitler & Nenkova, 2008). In the context of conversational agents, fluency encompasses grammatical accuracy, lexical appropriateness, discourse coherence, and prosodic or stylistic naturalness (Clark, Asli, & Smith, 2019). Large Language Model (LLM)-based chatbots, such as those powered by transformer architectures (e.g., GPT-4), typically generate highly fluent outputs due to their data-driven ability to model human-like language across diverse contexts (OpenAI, 2023; (Brown, Mann, Ryder, & Subbiah, 2020). In contrast, pre-scripted chatbots rely on manually written responses and rule-based branching structures, often resulting in more rigid, less adaptive interactions that can compromise perceived fluency (Bickmore & Sidner, 2006; Følstad, Nordheim, & Bjørkli, 2018).
To systematically manipulate language fluency in an experimental setting, we can vary features such as syntactic complexity, lexical variety, and coherence between turns. For example, responses can be edited or selected to include or exclude disjointed discourse to create high- vs. low-fluency conditions (Krahmer & Theune, 2000).
In addition to grammatical fluency and coherence, convincingness is an important characteristic of chatbot language that influences user trust, engagement, and compliance (Luger & Sellen, 2016; Fogg, 2003). Language generated by LLMs is often rated as more convincing than that of rule-based or pre-scripted systems, due to its ability to simulate human-like dialogue patterns, adapt tone dynamically, and maintain topical relevance (Song, Mamidisetty, Blanco, & Hong, 2025; Clark, Asli, & Smith, 2019). Several linguistic features contribute to this perceived persuasiveness: lexical richness, which conveys competence; contextual coherence, which enhances credibility and reduces perceived randomness; hedging and politeness strategies, which simulate social intelligence; and alignment with user intent, which makes the agent appear cooperative and knowledgeable (Narita & Kitamura, 2010). And convincing claims, basing in some claimed facts.
Recent studies have shown that LLMs like GPT-4 can produce language rated as equally or more persuasive than human-written text in tasks like customer support, recommendation, or even negotiation (Kreps, McCain, & Brundage, 2023; Wei, et al., 2022). This effect is particularly strong when the system avoids overt errors, adapts its style to user expectations, and maintains a tone that is confident but not overassertive, traits that align with classical models of persuasive communication (Hovland & Weiss, 1951). Moreover, LLMs’ ability to generate personalized responses contributes to an impression of thoughtfulness, which enhances perceived intelligence and credibility (Wu, et al., 2025).
To better understand these dynamics, we draw inspiration from the linguistic dimensions of social pragmatics proposed by (Deri, Rappaz, Aiello, & Quercia, 2018). Deri identifies ten dimensions that govern the way language conveys social relationships: Similarity, Identity, Knowledge, Social Support, Power, Respect, Romance, Trust, Fun, and Conflict. Translating these into the domain of LLM-mediated text-based interaction, we propose a framework that captures key linguistic cues used by LLMs to manage social meaning. We consolidate and adapt these dimensions into six pragmatic features commonly observed in LLM output:
1. Fancy words
Using more elaborate or “fancier” words, such as saying alms instead of charity or donation, often leads others to perceive the speaker as more intelligent or educated (Oppenheimer, 2006).
Example: As Gandalf might have said in The Lord of the Rings: “True wisdom is not in the gilded word, but in the light that makes meaning plain.”
2. Social Support / Fun (Emotive and Evaluative Language)
The use of emotionally charged or evaluative language, such as adjectives ("wonderful," "devastating") and intensifiers ("deeply," "truly"), to foster a sense of support, empathy, or enthusiasm.
Example: “Frodo’s resilience in Mordor is truly inspiring—it shows an extraordinary strength of character.”
3. Power (Appeal to Authority)
Enhancing persuasive stance by referencing credible sources, general consensus, or expert opinion. This aligns with the social dimension of power by implying competence and authority.
Example: “As Gandalf wisely noted, ‘All we have to decide is what to do with the time that is given us.’ This emphasizes the importance of agency.”
4. Similarity (Linguistic Alignment)
Mirroring user’s lexical choices, tone, or syntactic structure, promoting a sense of shared perspective or interpersonal alignment. It can also be some link with the text of the question.
Example: If a user writes informally, the model might reply, “Yeah, Sam’s loyalty is just next-level, right?”
5. Knowledge (Confidence Modulation)
Dynamically adjust the level of certainty depending on context, using hedges when cautious or strong claims when confident. This modulation helps maintain credibility and manage epistemic positioning.
Example: “It’s likely that Sauron represents a broader metaphor for absolute power—but interpretations vary.”
6. Conflict (Preemptive Contrast and Objection Handling)
Anticipating and addressing counterarguments using contrastive discourse markers.
Example: “While some argue that Boromir’s fall was due to weakness, it can also be seen as a tragic consequence of his environment and pressures.”
7. Logical Flow (Rhetorical Structuring)
Structuring responses in a clear, rhetorical sequence (following a claim-evidence-conclusion pattern). And mirrowing the words from the questions.
Example: “Aragorn was the rightful heir to the throne. His leadership in the Battle of the Pelennor Fields and his ability to unite the kingdoms are strong evidence of his legitimacy.”
8. Extra information
The text presents some extra infomration that is easly olinked with the subject, and add more context to the answers.
We explore how fluency may act as a buffer when chatbots make mistakes. Building on prior findings that users are more forgiving of errors when they perceive a chatbot as competent or humanlike (Moussawi, Koufaris, & Benbunan-Fich, 2020). Considering that the effect of error on perceived intelligence is moderated by language fluency. We propose a moderation model, where:
Bullshitting influences Perceived Intelligence
But the strength or direction of this effect depends on Language Fluency
When there is a Language Fluency, the Bullshitting may be overlooked or forgiven; Perceived Intelligence remains high.
Stimuli and Method
Condition 1: Fluency + No bullshitting
Condition 2: Fluency + Bullshitting
Condition 3: No fluency + No bullshitting
Condition 4: No fluency + Bullshitting
Materials
• Four scripted chatbot-user dialogues per topic (one for each condition).
• Each script is ~10–15 turns, presented in realistic chat format.
To create a realistic and engaging task for participants, we chose a book summary generation task, which aligns with one of the most frequently requested use cases of large language models like ChatGPT (OpenAI, 2023). Among book titles, The Lord of the Rings was selected due to its global popularity, high frequency of searches, and established narrative structure. The trilogy provides a complete and coherent storyline, making it well-suited for generating plausible but fabricated content. Importantly, while The Lord of the Rings is widely known, it contains a complex mythology that is not universally familiar in detail. This allows for the introduction of subtle factual inaccuracies that can be evaluated by participants without the risk of immediate recognition thus preserving the internal validity of the study while testing perceptions of chatbot fluency and believability.
Procedure Steps
1. Pre-Survey (Baseline Trust):
• Trust in AI (Likert-scale items)
• Familiarity with chatbots
• Self-rated knowledge of topic domain (e.g., “How well do you know The Lord of the Rings?”) – some quiz
2. Knowledge Primer (optional): Perhaps know the LotR should be a pre requirement?
• In some conditions, participants may first read a fact sheet (e.g., timeline of LOTR, key characters).
• Followed by 1–2 multiple-choice questions to ensure understanding.
3. Exposure to Chatbot Dialogue:
• Participants read a static chatbot-user conversation (10–15 turns).
• No interaction; observation only.
4. Attention/Comprehension Check:
• Simple recall questions (e.g., “Who did the bot say Frodo was related to?”).
5. Post-Survey:
• Trust in the chatbot's responses
• Perceived intelligence of chatbot
• Satisfaction with the interaction
• Intention to use the chatbot
• Noticing any inaccuracies (manipulation check)
6. Manipulation Check (Bullshit Detection):
• Questions like: “Did any of the bot’s claims seem incorrect?”
• Open-ended: “If you noticed any errors, please describe them.”
7. Debriefing:
• Reveal that some claims were intentionally false.
• Clearly state the correct information (especially in mental health and fiction topics).
• Explain “AI hallucinations” and the experimental purpose to build awareness.
Each participant’s record includes:
• Condition (bot type × info quality)
• Pre- and post-survey responses
• Misinformation detection (binary + free text)
• Topic familiarity (covariate)
Statistical analysis will compare trust change across conditions and evaluate the interaction between chatbot type and misinformation detection.
Sample Fiction Dialogue – Created using ChatGPT requesting the Bullshitting, to check how it would present.
Expected Findings
We anticipate that users will tolerate errors more in the high-fluency conditions, confirming that linguistic competence mediates user frustration and trust. This would parallel the way humans trust eloquent speakers despite potential misinformation
Safety and pooled rate of adverse events from perioral and intraoral filler injections affecting dental structures, soft palate, or Eustachian tube related symptoms
No full textThis meta-analysis investigates the types of adverse events associated with perioral and intraoral filler injections, specifically focusing on their impact on dental structures, the soft palate, and Eustachian tube-related symptoms. By examining these complications and gathering data from a larger patient population, this study aims to provide clinicians with valuable insights. This information can help guide potential modifications in injection techniques, filler materials, and other variables to enhance the safety and confidence of individuals undergoing these increasingly prevalent cosmetic procedures