Juji, Inc.

Unlocking the Future of Upskilling with Generative AI Learning Companions As technology and markets rapidly evolve, continuous learning is more critical than ever. However, many talent development programs are falling short of employee expectations. Skillsoft’s recent survey of 2,500 full-time employees across US, UK, Germany, and India sheds light on key barriers to effective upskilling (Image 1): 1. Lack of Time – 42% of respondents cited time constraints as the top obstacle. 2. Limited Options & Irrelevant Content – 30% noted insufficient training options, and 26% found the content misaligned with their needs. 3. Poor Leadership Support – 26% highlighted inadequate backing from leadership. 4. Limited Budget – 24% cited budget constraints affecting training initiatives. https://lnkd.in/ejsjk_MV Generative AI learning companions have the potential to transform upskilling by addressing these challenges: 1. Time Efficiency AI learning companions can offer on-demand, 24x7 learning, enabling employees to upskill whenever they have free time. This will also blur the boundary of formal and informal learning and foster continuous learning. 2. Content Personalization By analyzing individual learning needs and gaining a deep understanding of each individual's background, interest, and personality, AI learning companions can dynamically adapt learning content to each learner and recommend suitable learning resources to make learning more relevant, engaging, and effective (Image 2). 3. Enhanced Support AI learning companions can provide 24x7 learning support, from answering learner questions to brainstorming with learners on their real-world challenges, creating a more immersive and motivating learning experience (Image 3). 4. Optimized Business ROI By handling routine coaching tasks and enhancing scalability, AI-powered learning can scale out human coaching efforts, accelerating skill development, and boosting workforce competency organization-wide. As Skillsoft’s findings suggest, it’s time for organizations to rethink their upskilling strategies. Incorporating generative AI into training initiatives can create a more engaging, efficient, and tailored learning experience, empowering employees and fostering a more resilient workforce for the future. #Upskilling #GenerativeAI #AIChatbots #TalentDevelopment #EmployeeTraining #ContinuousLearning #Skillsoft

Addressing Bias in Large Language Model (LLM) Generated Content As AI continues to shape various fields, 2 recent studies shine a spotlight on a critical issue: bias in content generated by large language models (LLMs). In this post, we highlight the extent of biases in LLM-generated content and discuss why it is crucial to address this issue. Key Study Findings: 1. Bias in AI-Simulated Public Opinions (Yao Qu & Jue Wang at Nanyang Technological University Singapore): The study evaluates ChatGPT’s ability to simulate public opinion using socio-demographic data from the World Values Survey. Results reveal that LLMs perform better in Western, English-speaking countries like the U.S., but struggle in non-Western and less developed nations. The study also identifies demographic biases related to gender, ethnicity, and social class, demonstrating that LLMs tend to reinforce biases across different themes, particularly in political and environmental simulations. 2. Bias in AI-Generated News (Xiao Fang at University of Delaware, Shangkun Che at Tsinghua University, Minjia Mao at University of Delaware, 张泓哲 at The Chinese University of Hong Kong, Ming Z., & xiaohang zhao at Shanghai University of Finance and Economics): This study investigates bias in AI-generated content produced by several LLMs, including ChatGPT and LLaMA. The study found significant gender and racial biases in AI-generated news articles in comparison with human-created new articles of the same headlines, particularly discrimination against females and individuals of Black descent. Why Does Bias Occur? LLMs are trained on vast amounts of text data scraped from the internet, which often includes biased or skewed information based on existing social and cultural norms. Because these models lack the ability to inherently filter out biased content, they can replicate and even amplify those biases in their generated content. Why is Bias a Problem? On the one hand, bias in AI-generated content can lead to the reinforcement of harmful stereotypes and misinformation, potentially skewing public opinions or influencing individuals. On the other hand, if AI-generated content is used to train future models, this biases becomes cyclical, with LLMs learning from their own flawed outputs, leading to “model collapse,” where future iterations of the models lose diversity and accuracy. As AI-generated content becomes more prevalent, it’s essential to train LLMs using diverse, high-quality, human-generated content that represents a wide range of perspectives to mitigate biases in LLMs and ensure that AI is a tool for positive and equitable changes. What are your thoughts on addressing bias in AI-generated content? Let’s discuss! More details of the studies: https://lnkd.in/gPxc2fK5 https://lnkd.in/gEvRH3R9 #AI #ArtificialIntelligence #BiasInAI #LLM #GenerativeAI #MachineLearning #TechEthics #ResponsibleAI #DiversityInAI #HumanInTheLoop

The Impact of AI-Generated Content on the Quality of Large Language Models While large language models (#LLMs) are becoming increasingly powerful, new challenges are emerging, especially regarding the quality of training data. Several recent studies highlight 2 critical issues that have far-reaching implications for the future development of effective LLMs. 1. Negative Impact of AI-Generated Content in Training Data One of the most significant concerns is how LLMs trained on AI-generated data begin to degrade in performance over time. A study by Ilia Shumailov, Zakhar Shumaylov, Yiren (Aaron) Zhao, Nicolas Papernot, Ross Anderson, and Yarin Gal at University of Oxford, University of Cambridge, Imperial College London, University of Toronto respectively describes this phenomenon as "model collapse." As LLMs generate more and more content that becomes part of the internet's data ecosystem, the models begin to lose the "tails" of the original human-generated content distribution. The nuanced diversity and richness of human language start to disappear, resulting in irreversible defects in the models. This research emphasizes that continued training on AI-generated content leads to poorer performance, particularly in capturing the complexity of genuine human language. 2. Declining Human-Generated Content Equally concerning is the decline in the availability of human-generated content. A study led by R. Maria del Rio-Chanona at UCL, Nadzeya Laurentsyeva at Ludwig-Maximilians-Universität München and Johannes Wachs at Corvinus University of Budapest revealed that the release of ChatGPT coincided with a sharp 25% decrease in activity on Stack Overflow, a key platform for publicly sharing coding knowledge. The decrease was larger in areas where ChatGPT excels, such as beginner-level programming questions, and occurred across both experienced and novice users. This suggests that AI tools are not just filling gaps but are actively reducing the production of the human-generated content that LLMs rely on for future training. Without a robust supply of diverse, high-quality human data, future LLMs risk becoming less effective and biased. These studies underscore the importance of curating diverse, high-quality human-generated content for the continued advancement of LLMs. Without careful integration of authentic human interactions, the ability of LLMs to understand and mimic complex human language will deteriorate. Additionally, as human-generated content diminishes, the available training data for these models shrinks, limiting future improvements. What are your thoughts on these challenges? How can we ensure that LLMs continue to be trained on diverse, high-quality human data? Let’s discuss! More details of the studies: https://lnkd.in/g52G2JRR https://lnkd.in/gRhvYzw4 #AI #LLMs #MachineLearning #GenerativeAI #ChatGPT #DataQuality #AIGeneratedData #ModelTraining #HumanGeneratedData

Generative AI in Learning Design: Key Takeaways from Khan Academy’s Khanmigo AI holds incredible promise for transforming learning design, but it's not without its challenges. In a recent blog post, Kristen Eignor DiCerbo, Chief Learning Officer at Khan Academy, shared valuable insights on their journey using generative AI in the Khanmigo tool to create lesson plans. Outlined below are three challenges they encountered: https://lnkd.in/eukifbgJ 1. Subpar quality of lesson plans While AI can generate lesson plans quickly, early attempts with Khanmigo revealed some quality issues: • Lesson objectives often just repeated the standard. • Warmups didn’t consistently address key prerequisite skills. • Some answer keys for practice exercises were incorrect. • Lesson sections varied in length and format, leading to a lack of coherence. • AI occasionally ignored parts of the prompt instructions. 2. Lack of contextual knowledge and domain expertise The AI's limited understanding of detailed nuances like state standards, target grade levels, and subject-specific expertise often led to lesson plans that were too vague or inaccurate, reducing their practical value for teachers. 3. Prompt engineering complexity Attempting to generate comprehensive lesson plans with a single prompt led to results with neglected, unfocused, or entirely missing parts. Breaking the prompt into smaller, more focused sections produced better results with improved consistency and detail. Khan Academy's experience emphasizes the importance of combining AI with human expertise, as well as the traditional design wisdom: divide and conquer. While AI can generate draft lesson plans, educators play a crucial role in reviewing and refining the output to ensure it aligns with educational goals and quality standards. A human-in-the-loop approach is essential for leveraging AI's speed and scale while maintaining the accuracy and relevance needed in education. Generative AI has the potential to be a game-changer in learning design, but success lies in the collaboration between technology and human expertise. Platforms like Khanmigo demonstrate that, while AI can provide the base, it's the human touch that turns it into a breakthrough learning experience. What do you think are the biggest challenges in using AI for educational content creation? Let’s discuss in the comments! #EdTech #GenerativeAI #AIInEducation #LearningDesign #InstructionalDesign #AIChatbots #HumanInTheLoop #KhanAcademy #AI #ChatbotDesign

Exploring the Impact of AI Agent Language Complexity and Persona in Healthcare As AI agents (often in the form a chatbot) continue to revolutionize healthcare, designing them for maximum impact is more critical than ever. A recent study by Joshua Biro, PhD, Courtney Linder, and David Neyens from Clemson University examined the effects of a healthcare chatbot’s language complexity (technical vs. non-technical) and persona (Doctor, Nurse, or Nursing Student) on user trust, perceived usability, and effectiveness. Here are the key findings: • Language Complexity: Using technical language significantly increased the chatbot’s perceived effectiveness, but it had no impact on user trust or perceived usability. • Chatbot Persona: The persona of the chatbot (Doctor, Nurse, or Nursing Student) significantly influenced its perceived usability, yet it did not affect the chatbot’s effectiveness or user trust. While these findings might not apply universally, they underscore the importance of thoughtful design of a chatbot’s language complexity and persona to enhance its ability to deliver healthcare information effectively. As chatbots play a growing role in healthcare, leveraging these insights can help shape future AI tools to deliver patient-centered care that meets diverse needs. Designing healthcare AI agent chatbots with the right balance of technical language and relatable persona can lead to better user engagement, increased trust, and more effective healthcare support. Fortunately, with conversational AI platforms powered by generative AI, such as Juji Studio, it’s now easier than ever to create and experiment with chatbots that vary in language complexity and persona to find that perfect balance. What do you think are the most important aspects of chatbot design in healthcare? Let’s discuss in the comments! More details: https://lnkd.in/ea6Miyr6 #HealthcareAI #AIChatbots #ConversationalAI #DigitalHealth #AIinHealthcare #ChatbotDesign #PersonaDesign

AI Chatbot Agents in Non-Diagnostic Patient-Centered Care: A High-ROI, Low-Risk Opportunity While AI-driven diagnostics and treatment planning often grab the spotlight, interactive AI agents in the form a chatbot can be game-changers in non-diagnostic, patient-centered care—offering significant benefits with lower risk. A recent article by Sam Schwager on SuperDial explored how these intelligent agents can transform everyday health management. 1. Enhancing Daily Health Monitoring AI chatbots can make it easier for individuals to proactively manage their health. They can help users track symptoms, manage medication, and log daily activities, providing a more comprehensive view of their well-being. Key examples include: • Symptom Tracking: Regularly log symptoms to identify patterns that can be shared with healthcare providers. • Mood and Well-being Monitoring: Track emotional health to support mental wellness. 2. Streamlining Medication Management AI chatbots can play a significant role in ensuring medication adherence, a crucial factor for chronic disease management: • Sending Medication Reminders: Prompt users to take their meds on schedule. • Providing Medication Information: Offer insights on potential side effects or interactions. 3. Supporting Lifestyle and Chronic Disease Management AI chatbots are powerful tools for chronic disease management, providing real-time data and personalized feedback: • Activity and Nutrition Logging: Track exercise and diet for better disease management. • Vital Sign Monitoring: Keep tabs on key metrics like blood pressure or glucose levels, alerting users to abnormalities. 4. Connecting Patients with Healthcare Providers AI chatbots can enhance communication between patients and healthcare providers, improving care coordination for more informed decisions. 5. Keeping Patients Informed AI chatbots can empower users with timely health information, personalized tips, and updates on treatment options, helping them make informed choices about their care. By focusing on these non-diagnostic use cases, AI chatbots can reduce the strain on healthcare providers and empower patients to take a more active role in managing their health. This approach benefits both patients and the healthcare system, driving better outcomes and improving efficiency. What are your thoughts on AI chatbots in patient-centered care? Have you ever used AI chatbots in such care routines? What's your experience like? Share your insights in the comments below! For more details: https://lnkd.in/eREFsP4F #AIinHealthcare #ConversationalAI #PatientCare #DigitalHealth #HealthcareInnovation #HealthTech #Chatbots

Can ChatGPT Mimic Humans in Personality Assessment: New Insights from a Recent Study A recent study, led by researchers Pengda Wang and Tianjun Sun from Rice University, Huiqi Zou and Ziang Xiao from The Johns Hopkins University, Zihan Yan and Bo Zhang from University of Illinois Urbana-Champaign, and Feng Guo from The University of Tennessee at Chattanooga, examined whether LLMs could simulate human responses accurately enough to replace people in psychological studies, using the well-known Big Five and HEXACO personality tests as benchmarks. The study revealed that while LLMs can mimic human responses and capture broad personality traits, they struggle to grasp the finer details that are critical for accurate personality assessments. Furthermore, it found that LLMs have a strong tendency towards social desirability bias—often providing responses that sound more socially acceptable rather than reflecting genuine, diverse human experiences. As a result, the researchers concluded that LLMs, despite their advanced capabilities, cannot yet replace human respondents in psychometric research. The challenges faced by LLMs in generating accurate simulated responses also suggest limitations in their ability to provide reliable personality assessments based on real human conversations. The study highlights the current gaps in LLM-based personality assessments and the need of realistic conversational data for reliable personality assessment. What are your thoughts on the future of AI in personality assessment? Let’s continue the conversation in the comments below! For more details of the study: https://lnkd.in/gct9EkGZ #AIResearch #PersonalityAssessment #LLM #Psychometrics #ConversationalAI #Juji #MachineLearning

3 Takeaways from PVCP's Webinar on Bringing Conversational Intelligence to Healthcare In a recent webinar hosted by Project Voice Capital Partners's Bradley Metrock, with guest speakers Dr. Yaa Kumah-Crystal, MD MPH FAMIA of Vanderbilt University and Amy Brown, CEO of Authenticx, the growing role of conversational intelligence in healthcare was explored. The discussion covered conversational AI and its applications in healthcare, including adoption strategies and challenges. https://lnkd.in/ePQgbbR9 Here are the key takeaways: 1. New Use Cases of AI in Healthcare -Proactive Patient Education: AI can play a pivotal role in guiding patients to ask relevant follow-up questions based on their medical history. This helps patients better educate themselves and communicate more effectively with healthcare providers. -Clinical Insights from Operational Data: AI can pull insights from non-clinical interactions, such as billing conversations, to reveal important clinical information. For example, a patient discussing payment options may flag treatment adherence issues. 2. Key Considerations for Adopting AI in Healthcare -Change Management: AI adoption must focus on strategic alignment and managing the human aspect of change. Organizations need to clearly define their business goals, determine how AI can help achieve them, and ensure that all stakeholders have a unified understanding of AI's role and expected outcomes. -Vendor Evaluation: Healthcare organizations must rigorously assess AI vendors for data privacy, cybersecurity, and platform integrity. Conducting thorough evaluations and research is crucial to selecting AI solutions that offer improvements over existing tools and meet the organization's needs. 3. Challenges in AI Adoption in Healthcare Conversations -Defining Success Benchmarks: One challenge is creating appropriate benchmarks to evaluate AI performance. Early adopters must have a vision while being realistic about the technology's current capabilities. -Balancing Innovation with Safety: While there is significant interest in AI's potential, healthcare organizations are also cautious, ensuring that AI is deployed with the highest standards of security and patient safety. -Education and Demystification of AI: Healthcare workers need to understand AI's role in their workflows. Educating staff about how AI can complement their work will help reduce confusion and foster a more collaborative human-AI relationship, empowering healthcare professionals to leverage AI as a valuable tool. AI holds immense promise in healthcare, but its successful adoption requires a balanced approach—aligning organizational goals, ensuring privacy and safety, and fostering a well-educated workforce ready to collaborate with AI tools. #AIinHealthcare #ConversationalIntelligence #HealthcareInnovation #AIAdoption #AIBenchmarks #AIGovernance #HealthcareAI

Leveraging Conversational AI to Unlock the Future of Experiential Learning Higher ed institutions are increasingly incorporating AI into their curricula to offer experiential learning opportunities, helping students build essential soft skills in a more interactive way. Several universities have integrated AI-driven simulations in their programs: • Touro University has launched an online social work program that includes AI-assisted clinical simulations. Students practice conversations with patients through selecting prompts and receiving feedback, honing key skills such as patient interaction, ethical decision-making, and clinical assessments. https://lnkd.in/e-_h7vvv • Hult International Business School enables marketing students to use AI-powered simulations for market research, engaging with simulated customers in realistic environments. https://lnkd.in/egQHbhbr • Indiana University – Purdue University Indianapolis uses AI-powered audiences to help students improve public speaking and interview skills, creating a space for students to practice critical communication techniques. https://lnkd.in/gVTHA35b These AI-powered tools create safe, interactive environments where students can build confidence, receive immediate feedback, and practice real-world scenarios without the pressure of working with live patients or clients. However, there are limitations that AI advancements—especially conversational AI agents—can address: 1. More Dynamic Responses from Students: Many current simulations rely on pre-set multiple-choice response options. Conversational AI agents can engage in free-flowing dialogue with students, encouraging them to think critically and craft their own responses. 2. Deeper, More Tailored Feedback: While current simulations offer immediate feedback, it is often generic and lacks depth. Conversational AI agents can provide more nuanced, real-time feedback tailored to individual student responses, helping them understand why their answers are correct/incorrect and how to improve their performance. 3. Personalized Learning: Most simulations adopt a one-size-fits-all approach, offering the same experience to all students. Conversational AI agents can tailor simulations to each student's learning style, pace, strengths, areas for improvement, and personal situation or challenges, creating a more effective and customized learning experience. By integrating conversational AI agents into experiential learning platforms, institutions can offer more immersive, personalized, and dynamic learning experiences to help students better prepare for future careers. #AIinEducation #ExperientialLearning #ConversationalAI #EdTech #PersonalizedLearning

Two Critical Keys to Building Successful AI Agents: Human-in-the-Loop and the Power of Partnerships Although more and more companies are looking to using AI agents to automate workflows and boost productivity, Forrester warns that 75% of organizations attempting to build AI agents in-house will fail due to the complexity and specialized expertise required. A recent article by Grant Gross, published on CIO Online, highlighted two critical keys to success: https://lnkd.in/efB24HcP 1. Human-in-the-Loop Even the most advanced AI requires human oversight, not to mention that AI is far from perfect. AI agents need ongoing supervision, monitoring, and improvements to ensure that they accomplish intended tasks and deliver desired ROI. A human-in-the-loop approach not only safeguards AI behavior but also helps AI agents learn and adapt more effectively over time. 2. Partner with a Trusted AI Provider Building AI agents is complex, especially when it comes to the support of nuanced business tasks, such as those requiring advanced interaction management including memory and context management. It involves more than just storing and retrieving data—it requires "interactional intelligence" beyond just language intelligence supported by large language models (#LLMs). For example, AI agents need to understand the relevance of past interactions, and dynamically adapt responses as knowledge evolves. Partnering with an experienced AI provider gives organizations access to proven solutions, reducing the risk of failure and saving time. Juji Studio, a #NOCODE platform for building practical and interactive AI agents, offers a comprehensive solution by automating memory and context management while adapting content dynamically. It enables the creation of AI agents with both interactional intelligence (Image 1-3) and personal intelligence (Image 4-6). By adopting a human-in-the-loop approach, Juji ensures content accuracy, and reliability and controllability of agent behavior, making it easier for organizations to deploy AI agents without the complexities of building from scratch. To overcome the challenges of building AI agents, it’s essential to incorporate human oversight and leverage AI platforms like Juji Studio. This strategy ensures faster deployment, reliable performance, and continuous improvement—without the risks of going it alone. #AI #AgenticAI #HumanInTheLoop #AIAutomation #JujiStudio #NoCode #AIProviders #AIinBusiness #DigitalTransformation