202,249 research outputs found
AI for L&D - eBook
No full textAI in L&D - A Comprehensive Guide to Transformation and Growth eBook
Step into the future of learning with AI. Explore its role in revolutionizing workflows, creating innovative eLearning and boosting learning quality. This eBook is a roadmap to discover the perfect synergy between AI and L&D!
The advent of Generative Artificial Intelligence (AI) has been a catalyst for innovation and growth across a multitude of industries. This eBook explores, suggests and foretells the impact of AI in the field of Learning & Development (L&D). Artificial Intelligence is not a completely new concept – there have been AI-enabled platforms around. However, the easy access to the power of Generative AI for a typical L&D professional changes the landscape significantly.
This new synergy between AI and L&D is reshaping the educational landscape, opening doors to personalized learning experiences, enhancing engagement, and fostering a culture of continuous growth and adaptability without the need for expensive stand-alone platforms.
In this eBook, you'll read about:
Using AI in workflows and learning products
Leveraging AI in eLearning and online learning
Managing challenges, security, and ethical considerations
Understanding whether AI in eLearning is too early, too much, or just right for yo
AI and the Everyday Writer
No full textBased on a qualitative study of workplace writers, we argue that theories of language and AI must account for the activity of uptake and implementation on the ground, which, at least in the near future, will be messy, incomplete, uneven, chaotic, and perhaps even boring
AI and Human Reasoning: Qualitative Research in the Age of Large Language Models
Full text linkContext: The advent of AI-driven large language models (LLMs), such as ChatGPT 3.5 and GPT-4, have stirred discussions about their role in qualitative research. Some view these as tools to enrich human understanding, while others perceive them as threats to the core values of the discipline. Problem: A significant concern revolves around the disparity between AI-generated classifications and human comprehension, prompting questions about the reliability of AI-derived insights. An “AI echo chamber” could potentially risk the diversity inherent in qualitative research. A minimal overlap between AI and human interpretations amplifies concerns about the fading human element in research. Objective: This study aimed to compare and contrast the comprehension capabilities of humans and LLMs, specifically ChatGPT 3.5 and GPT-4. Methodology: We conducted an experiment with small sample of Alexa app reviews, initially classified by a human analyst. ChatGPT 3.5 and GPT-4 were then asked to classify these reviews and provide the reasoning behind each classification. We compared the results with human classification and reasoning. Results: The research indicated a significant alignment between human and ChatGPT 3.5 classifications in one-third of cases, and a slightly lower alignment with GPT-4 in over a quarter of cases. The two AI models showed a higher alignment, observed in more than half of the instances. However, a consensus across all three methods was seen only in about one-fifth of the classifications. In the comparison of human and LLMs reasoning, it appears that human analysts lean heavily on their individual experiences. As expected, LLMs, on the other hand, base their reasoning on the specific word choices found in app reviews and the functional components of the app itself. Conclusion: Our results highlight the potential for effective human-LLM collaboration, suggesting a synergistic rather than competitive relationship. Researchers must continuously evaluate LLMs’ role in their work, thereby fostering a future where AI and humans jointly enrich qualitative research.</jats:p
Meaningful human control: actionable properties for AI system development
No full textHow can humans remain in control of artificial intelligence (AI)-based systems designed to perform tasks autonomously? Such systems are increasingly ubiquitous, creating benefits - but also undesirable situations where moral responsibility for their actions cannot be properly attributed to any particular person or group. The concept of meaningful human control has been proposed to address responsibility gaps and mitigate them by establishing conditions that enable a proper attribution of responsibility for humans; however, clear requirements for researchers, designers, and engineers are yet inexistent, making the development of AI-based systems that remain under meaningful human control challenging. In this paper, we address the gap between philosophical theory and engineering practice by identifying, through an iterative process of abductive thinking, four actionable properties for AI-based systems under meaningful human control, which we discuss making use of two applications scenarios: automated vehicles and AI-based hiring. First, a system in which humans and AI algorithms interact should have an explicitly defined domain of morally loaded situations within which the system ought to operate. Second, humans and AI agents within the system should have appropriate and mutually compatible representations. Third, responsibility attributed to a human should be commensurate with that human’s ability and authority to control the system. Fourth, there should be explicit links between the actions of the AI agents and actions of humans who are aware of their moral responsibility. We argue that these four properties will support practically minded professionals to take concrete steps toward designing and engineering for AI systems that facilitate meaningful human control.Interactive IntelligenceDesign AestheticsCyber SecurityHuman-Robot InteractionEthics & Philosophy of TechnologyHuman Information Communication DesignWeb Information System
Insemination factors affecting the conception rate in seasonal calving Holstein-Friesian cows
No full textpeer-reviewedDifferences in conception rate to first service between artificial inseminations (AI) carried out by commercial AI operators (CAI) or do-it-yourself operators (DIY), between natural service (NAT) and AI, between different AI sires, and between fresh and frozen-thawed semen, on Irish commercial dairy farms, were studied using logistic regression. The study comprised 12 933 potential first inseminations from 77 spring-calving dairy herds. The data were recorded during 1999 and 2000. Amongst the total, 4 394 cows had repeated records across the two years. Adjustment variables included: herd, year, parity, calving period, calving to service interval, herd size, proportion of North American Holstein-Friesian genes, peak milk yield, semen fresh or frozen-thawed status, AI sire and a cow history variable to account for the correlation structure that may exist between performance records of cows present in both years of the study. Interactions of interest were tested but were non-significant. No significant association was observed between the category of AI operator and the likelihood of conception rate to first service (PREG1). The variation in PREG1 observed within the category of operator (CAI and DIY) was investigated using the Levene test for homogeneity of variance. There was no difference between the level of variation observed within CAI and DIY operators. There were significant differences in the likelihood of PREG1 between different AI sires. Amongst the 40 most commonly used AI sires, 3 sires had a lower likelihood of PREG1 (P < 0.05) when compared to the reference AI sire (sire with PREG1 similar to the mean of the group). There was a tendency for a reduced likelihood of PREG1 with the use of fresh semen compared to frozen-thawed semen ( , P = 0.067). Amongst the adjustment variables in the model, those significantly associated with the likelihood of PREG1 included the herd, calving period, calving to first service interval and peak milk yield. No significant difference in the likelihood of PREG1 was observed between AI and NAT.AIB Bank; Holstein UK and Ireland; the
National AI Co-ops; Dairy Levy Fund (Ireland
Visual-AI-d
No full textby Earth Refocus Institute (ERI)
In today’s increasingly remote and digital world, visual collaboration has become a cornerstone of productivity. However, despite the wide adoption of screen-sharing tools, many platforms lack the AI-driven functionality needed to take collaboration to the next level. This is where Visual-AI-d comes in—a tool designed to integrate real-time screen sharing with AI-powered visual assistance, dramatically improving efficiency and accuracy in collaborative tasks.
What is Visual-AI-d? Visual-AI-d is a new approach that enhances workflows by providing instant, context-based AI feedback during visual collaboration. The system works by capturing real-time screen activity, interpreting data using AI algorithms, and offering suggestions or corrections on the fly. This reduces task completion time and improves the overall quality of work, making it a game-changer for industries that rely heavily on visual problem-solving, such as design, engineering, and education.
How Does Visual-AI-d Work? Visual-AI-d operates through three core components:
Real-Time Screen Capture: Continuously captures screen activity during collaboration without noticeable latency.
AI Visual Interpretation Algorithms: These algorithms are trained to recognize patterns and provide task-specific assistance.
User-Friendly Interface: Visual-AI-d provides unobtrusive feedback via overlays or tooltips, ensuring a smooth, intuitive user experience.
Key Results and Findings In a comparative study, we evaluated the impact of Visual-AI-d on task performance. Results showed:
For simple tasks, the time was reduced by approximately 93%.
For complex tasks, the time was reduced by 66%.
Participants using Visual-AI-d also experienced lower error rates and reported higher satisfaction.
The Future of AI-Driven Collaboration The implications of Visual-AI-d are significant, particularly for large organizations. By reducing task completion times and enhancing accuracy, companies can save thousands of hours in productivity gains. As AI continues to evolve, tools like Visual-AI-d will play an increasingly important role in shaping how we collaborate and innovate in the workplace.
Conclusion
Visual-AI-d isn’t just another screen-sharing tool—it’s a novel approach to transforming the way we collaborate on visual tasks. As remote work and digital collaboration become the new norm, the need for AI-driven solutions has never been clearer. Visual-AI-d is leading the way toward a future of seamless, intelligent collaboration
Cultural Representativeness in the Principles of AI
No full textArtificial intelligence (AI) applications have reached a wide range of domains and have raised concerns over fairness, accountability, transparency, and ethics. For example, social media platforms face challenges from Congress over data privacy and facial recognition software have been racially biased. Accordingly, society is actively establishing principles to govern the development and application of AI technologies; examples include General Data Protection Regulation (GDPR). But, as AI innovation disseminates across cultural and political boundaries, how do societies in different cultures perceive these high-level AI principles? What are the acceptable ground rules for global AI governance? This project seeks to answer these by studying the interactions between cultural norms, the public opinion of AI, and the AI research community. To understand how key AI principles resonate amongst different cultures, we will study knowledge production and dissemination. Our approach is informed by the studies in comparative philosophy, which contrast moral traditions developed along relatively isolated cultural and regional lines. We will combine data science, qualitative studies, and mixed-methods approach to analyze micro- and macroscopic data. This project will build synergy among distinct disciplines represented by the team, including Philosophy and Ethics, Data and Information Sciences, Psychology, and Anthropology
AI Risk: A Systems Perspective
Full text linkWhile these principles have been established
as generic guidelines based on learning from
other domains, there is a lack of studies applying
these principles to actual AI development. In
practice, managing AI risks from a systems
thinking perspective involves a comprehensive
approach, starting from problem formulation to a
lifecycle approach, understanding and identifying
interactions, hyper-stakeholder management,
including participatory modelling, and anticipatory
thinking. In this study, we consider the risks and challenges
associated with AI by examining four compelling
case studies that the author had carried out in the
past. These case studies shed light on the potential
pitfalls and complexities that arise in the realm of
AI, allowing us to gain a deeper understanding of
the risks involved
Player agency in interactive narrative: audience, actor & author
Full text linkThe question motivating this review paper is, how can
computer-based interactive narrative be used as a constructivist learn-
ing activity? The paper proposes that player agency can be used to
link interactive narrative to learner agency in constructivist theory,
and to classify approaches to interactive narrative. The traditional
question driving research in interactive narrative is, ‘how can an in-
teractive narrative deal with a high degree of player agency, while
maintaining a coherent and well-formed narrative?’ This question
derives from an Aristotelian approach to interactive narrative that,
as the question shows, is inherently antagonistic to player agency.
Within this approach, player agency must be restricted and manip-
ulated to maintain the narrative. Two alternative approaches based
on Brecht’s Epic Theatre and Boal’s Theatre of the Oppressed are
reviewed. If a Boalian approach to interactive narrative is taken the
conflict between narrative and player agency dissolves. The question
that emerges from this approach is quite different from the traditional
question above, and presents a more useful approach to applying in-
teractive narrative as a constructivist learning activity
User Experience and AI-infused products. A wicked relationship
No full textThe current panorama of AI-infused devices portrays a significant dominance of first-party smart speakers, which appear to be the first massive embodiment of AI in the domestic landscape. These devices are nothing more than discreet ornaments, looking at their simple phys- ical appearance. Although, the simple appearance betrays a complexity determined by numerous features that make such products challenging to analyze from a UX point of view.
The main evident characteristic is that they are not just “simple products” but ecosystems consisting of several interfaces and touch- points. Most of them integrate multiple interfaces – namely physical, digital, conversational – sometimes overlapping.
The second element of complexity resides in their technological core, based on learning algorithms. Therefore, the same device can provide different outputs at the same input over time, a condition that can affect the user experience.
To increase the complexity of these devices, at least from a UX standpoint, there is the fact that their real potential is rarely exploited by most users, which mainly uses routine actions such as reading news, weather forecasting, and controlling simple home appliances.
Accordingly, the chapter frames the wicked relationship between user experience and AI-infused products. Moving from the three iden- tified elements of the complexity of AI-infused products, it advances reflection on how it could be possible to analyze these products from a UX standpoint
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