5,101 research outputs found
Towards a Psychological Generalist AI: A Survey of Current Applications of Large Language Models and Future Prospects
The complexity of psychological principles underscore a significant societal
challenge, given the vast social implications of psychological problems.
Bridging the gap between understanding these principles and their actual
clinical and real-world applications demands rigorous exploration and adept
implementation. In recent times, the swift advancement of highly adaptive and
reusable artificial intelligence (AI) models has emerged as a promising way to
unlock unprecedented capabilities in the realm of psychology. This paper
emphasizes the importance of performance validation for these large-scale AI
models, emphasizing the need to offer a comprehensive assessment of their
verification from diverse perspectives. Moreover, we review the cutting-edge
advancements and practical implementations of these expansive models in
psychology, highlighting pivotal work spanning areas such as social media
analytics, clinical nursing insights, vigilant community monitoring, and the
nuanced exploration of psychological theories. Based on our review, we project
an acceleration in the progress of psychological fields, driven by these
large-scale AI models. These future generalist AI models harbor the potential
to substantially curtail labor costs and alleviate social stress. However, this
forward momentum will not be without its set of challenges, especially when
considering the paradigm changes and upgrades required for medical
instrumentation and related applications
AI Chatbot for Generating Episodic Future Thinking (EFT) Cue Texts for Health
We describe an AI-powered chatbot to aid with health improvement by
generating Episodic Future Thinking (EFT) cue texts that should reduce delay
discounting. In prior studies, EFT has been shown to address maladaptive health
behaviors. Those studies involved participants, working with researchers,
vividly imagining future events, and writing a description that they
subsequently will frequently review, to ensure a shift from an inclination
towards immediate rewards. That should promote behavior change, aiding in
health tasks such as treatment adherence and lifestyle modifications. The AI
chatbot is designed to guide users in generating personalized EFTs, automating
the current labor-intensive interview-based process. This can enhance the
efficiency of EFT interventions and make them more accessible, targeting
specifically those with limited educational backgrounds or communication
challenges. By leveraging AI for EFT intervention, we anticipate broadened
access and improved health outcomes across diverse population
Interacting with educational chatbots: A systematic review
Chatbots hold the promise of revolutionizing education by engaging learners, personalizing learning activities, supporting educators, and developing deep insight into learners’ behavior. However, there is a lack of studies that analyze the recent evidence-based chatbot-learner interaction design techniques applied in education. This study presents a systematic review of 36 papers to understand, compare, and reflect on recent attempts to utilize chatbots in education using seven dimensions: educational field, platform, design principles, the role of chatbots, interaction styles, evidence, and limitations. The results show that the chatbots were mainly designed on a web platform to teach computer science, language, general education, and a few other fields such as engineering and mathematics. Further, more than half of the chatbots were used as teaching agents, while more than a third were peer agents. Most of the chatbots used a predetermined conversational path, and more than a quarter utilized a personalized learning approach that catered to students’ learning needs, while other chatbots used experiential and collaborative learning besides other design principles. Moreover, more than a third of the chatbots were evaluated with experiments, and the results primarily point to improved learning and subjective satisfaction. Challenges and limitations include inadequate or insufficient dataset training and a lack of reliance on usability heuristics. Future studies should explore the effect of chatbot personality and localization on subjective satisfaction and learning effectiveness
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Addressing barriers to learning: In the classroom and schoolwide.
IntroductionPublic education is at a crossroads. Moving in new directions is imperative. Just tweaking and tinkering with old ideas is a recipe for disaster.Continuing challenges confronting public education highlight why moving school improvement policy and practice in new directions is imperative. With a view to enhancing graduation rates and successful transitions to post-secondary opportunities and well-being, pressing challenges include:Increasing equity of opportunity for every student to succeed, narrowing the achievement gap, and countering the school to prison pipeline Reducing unnecessary referrals for special assistance and special education; Improving school climate and retaining good teachers Reducing the number of low performing schools.As education leaders well know, meeting these challenges requires making sustainable progress inimproving supports for specific subgroups (e.g., English Learners, immigrant newcomers, lagging minorities, homeless students, students with disabilities) increasing the number of disconnected students who re-engage in classroom learning and thus improving attendance, reducing disruptive behaviors (e.g., including bullying and sexual harassment), and decreasing suspensions and dropouts increasing family and community engagement with schools responding effectively when schools experience crises events and preventing crises whenever possible.In some schools, continuous progress related to these concerns is being made. For many districts, however, sustainable progress remains elusive – and will continue to be so as long as the focus of school improvement policy and practice is mainly on improving instruction. Efforts to expand the use of instructional technology, develop new curriculum standards, make teachers more accountable, and improve teacher preparation and licensing all have merit; but they are insufficient for addressing the many everyday barriers to learning and teaching that interfere with effective student engagement in classroom instruction.Most policy makers and administrators know that good instruction delivered by highly qualified teachers cannot ensure that all students have an equal opportunity to succeed at school.Even the best teacher can’t do the job alone. Teachers need student and learning supports in the classroom and schoolwide in order to personalize instruction and provide special assistance when students manifest learning, behavior, and emotional problems. Unfortunately, school improvement plans continue to give short shrift to these critical matters.We recognize, as did a Carnegie Task Force on Education, that school systems are not responsible for meeting every need of their students. But as the task force stressed: when the need directly affects learning, the school must meet the challenge.The most pressing challenge is to enhance equity of opportunity by fundamentally improving how schools address barriers to learning and teaching. The future of public education depends on moving in new directions to accomplish this.Now is the time to fundamentally transform how schools address factors that keep too many students from doing well at school. And while transformation is never easy, pioneering work across the country is showing the way. Trailblazers are redeploying existing funds allocated for addressing barriers to learning and weaving these together with the invaluable resources that can be garnered by collaboration with other agencies and with community stakeholders, family members, and students themselves.The first step in moving forward is to escape old ideas. The second step is to incorporate a new vision in school improvement planning for addressing barriers to learning and teaching and re-engaging disconnected students. Our analyses envision a plan that designs and develops a unified, comprehensive, and equitable system of student and learning supports. The third step is to develop a strategic plan for systemic change, scale-up, and sustainability.This book highlights each of these matters. We invite you to join us in the quest to enhance equity of opportunity for all students to succeed at school and beyond. And we look forward to hearing from you about moving schools forward to make the rhetoric of the Every Student Succeeds Act a reality
An Ontology-Based Recommender System with an Application to the Star Trek Television Franchise
Collaborative filtering based recommender systems have proven to be extremely
successful in settings where user preference data on items is abundant.
However, collaborative filtering algorithms are hindered by their weakness
against the item cold-start problem and general lack of interpretability.
Ontology-based recommender systems exploit hierarchical organizations of users
and items to enhance browsing, recommendation, and profile construction. While
ontology-based approaches address the shortcomings of their collaborative
filtering counterparts, ontological organizations of items can be difficult to
obtain for items that mostly belong to the same category (e.g., television
series episodes). In this paper, we present an ontology-based recommender
system that integrates the knowledge represented in a large ontology of
literary themes to produce fiction content recommendations. The main novelty of
this work is an ontology-based method for computing similarities between items
and its integration with the classical Item-KNN (K-nearest neighbors)
algorithm. As a study case, we evaluated the proposed method against other
approaches by performing the classical rating prediction task on a collection
of Star Trek television series episodes in an item cold-start scenario. This
transverse evaluation provides insights into the utility of different
information resources and methods for the initial stages of recommender system
development. We found our proposed method to be a convenient alternative to
collaborative filtering approaches for collections of mostly similar items,
particularly when other content-based approaches are not applicable or
otherwise unavailable. Aside from the new methods, this paper contributes a
testbed for future research and an online framework to collaboratively extend
the ontology of literary themes to cover other narrative content.Comment: 25 pages, 6 figures, 5 tables, minor revision
Ethical Framework for Harnessing the Power of AI in Healthcare and Beyond
In the past decade, the deployment of deep learning (Artificial Intelligence
(AI)) methods has become pervasive across a spectrum of real-world
applications, often in safety-critical contexts. This comprehensive research
article rigorously investigates the ethical dimensions intricately linked to
the rapid evolution of AI technologies, with a particular focus on the
healthcare domain. Delving deeply, it explores a multitude of facets including
transparency, adept data management, human oversight, educational imperatives,
and international collaboration within the realm of AI advancement. Central to
this article is the proposition of a conscientious AI framework, meticulously
crafted to accentuate values of transparency, equity, answerability, and a
human-centric orientation. The second contribution of the article is the
in-depth and thorough discussion of the limitations inherent to AI systems. It
astutely identifies potential biases and the intricate challenges of navigating
multifaceted contexts. Lastly, the article unequivocally accentuates the
pressing need for globally standardized AI ethics principles and frameworks.
Simultaneously, it aptly illustrates the adaptability of the ethical framework
proposed herein, positioned skillfully to surmount emergent challenges
Clinical proteomics for precision medicine: the bladder cancer case
Precision medicine can improve patient management by guiding therapeutic decision based on molecular characteristics. The concept has been extensively addressed through the application of –omics based approaches. Proteomics attract high interest, as proteins reflect a “real-time” dynamic molecular phenotype. Focusing on proteomics applications for personalized medicine, a literature search was conducted to cover: a) disease prevention, b) monitoring/ prediction of treatment response, c) stratification to guide intervention and d) identification of drug targets. The review indicates the potential of proteomics for personalized medicine by also highlighting multiple challenges to be addressed prior to actual implementation. In oncology, particularly bladder cancer, application of precision medicine appears especially promising. The high heterogeneity and recurrence rates together with the limited treatment options, suggests that earlier and more efficient intervention, continuous monitoring and the development of alternative therapies could be accomplished by applying proteomics-guided personalized approaches. This notion is backed by studies presenting biomarkers that are of value in patient stratification and prognosis, and by recent studies demonstrating the identification of promising therapeutic targets. Herein, we aim to present an approach whereby combining the knowledge on biomarkers and therapeutic targets in bladder cancer could serve as basis towards proteomics- guided personalized patient management
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