Ivancast Podcast

In this episode of our AI-focused season, SHIFTERLABS uses Google LM to unravel the groundbreaking research “How AI and Human Behaviors Shape Psychosocial Effects of Chatbot Use: A Longitudinal Randomized Controlled Study”conducted by researchers from the MIT Media Lab and OpenAI.

Over a span of 28 days and 300,000+ messages exchanged, 981 participants were immersed in conversations with ...

In this compelling episode of our research-driven season, SHIFTERLABS once again harnesses Google LM to decode the latest frontiers of human-AI interaction. Today, we explore “Investigating Affective Use and Emotional Well-being on ChatGPT,” a collaborative study by Jason Phang, Michael Lampe, Lama Ahmad, Sandhini Agarwal (OpenAI) and Cathy Fang, Auren Liu, Valdemar Danry, Samantha Chan, Pattie Maes (MIT Media Lab).

In this episode of our special season, SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. Today, we explore “AI Agents and Education: Simulated Practice at Scale”, a groundbreaking study by Ethan Mollick, Lilach Mollick, Natalie Bach, LJ Ciccarelli, Ben Przystanski, and Daniel Ravipinto from the Generative AI Lab at the Wharton School, University...

In this episode of our special season, SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. Today, we dive into the “International Scientific Report on the Safety of Advanced AI (2025)”, chaired by Prof. Yoshua Bengio and developed with contributions from 96 AI experts representing 30 countries, the UN, the EU, and the OECD.

In this episode of our special season, SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. Today, we dive into “Superagency in the Workplace: Empowering People to Unlock AI’s Full Potential”, a compelling study by Hannah Mayer, Lareina Yee, Michael Chui, and Roger Roberts, published in January 2025.

Drawing from exten...

In this episode of our special season, SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. Today, we explore “Emergent Misalignment: Narrow Finetuning Can Produce Broadly Misaligned LLMs”, a striking study by researchers from Truthful AI, University College London, the Center on Long-Term Risk, Warsaw University of Technology, the University of To...

In this episode of our special season, SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. Today, we dive into “Utility Engineering: Analyzing and Controlling Emergent Value Systems in AIs”, a pivotal study by researchers from the Center for AI Safety, the University of Pennsylvania, and the University of California, Berkeley.

In this episode of our special season, SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. Today, we explore the study “LLM Post-Training: A Deep Dive into Reasoning Large Language Models”, conducted by researchers from Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), the University of Oxford, the University of California Merced, ...

En este episodio de nuestra temporada especial, SHIFTERLABS aprovecha Google LM para desmitificar la investigación de vanguardia, traduciendo conocimientos complejos en ideas accionables. Hoy exploramos el estudio “LLM Post-Training: A Deep Dive into Reasoning Large Language Models”, elaborado por investigadores de la Universidad Mohamed bin Zayed de Inteligencia Artificial (MBZUAI), la Universidad de Oxford, la Unive...

In this episode of our ongoing season, SHIFTERLABS leverages Google LM to demystify cutting-edge research, translating complex insights into actionable knowledge. We dive into “The Widespread Adoption of Large Language Model-Assisted Writing Across Society”, a groundbreaking study by researchers from Stanford University, the University of Washington, and Emory University.

This research ex...

🚀 In this episode, we dive deep into the book “The Education Revolution Through Artificial Intelligence: Enhancing Skills, Safeguarding Rights, and Facilitating Human-Machine Collaboration” by Carlos Hervás-Gómez, M. Dolores Díaz-Noguera, and Fulgencio Sánchez-Vera. Published by Horizontes Universidad, this work critically examines how AI is transforming education, offering insights on its role in skills development,...

In this episode, we dive into the paper “Scaling Evidence-Based Instructional Design Expertise through Large Language Models” by Gautam Yadav from Carnegie Mellon University. This research explores how AI, particularly GPT-4, can transform instructional design by bridging the gap between educational theory and real-world application. Through two detailed case studies, we examine AI-driven content creation, active lear...

How can AI models improve their reasoning at test time without extensive retraining? In this episode, we explore the groundbreaking paper “Simple Test-Time Scaling” by researchers from Stanford University, University of Washington, and the Allen Institute for AI. This research introduces a novel approach to scaling reasoning performance by using extra compute at inference time, surpassing even OpenAI’s latest models.

In this episode, we explore OpenAI’s latest advancements in large reasoning models (LRMs) and their impact on competitive programming and software development. Based on the research paper “Competitive Programming with Large Reasoning Models” by OpenAI researchers, this discussion highlights how reinforcement learning is revolutionizing AI’s problem-solving capabilities.

From outperforming...

In this episode, we dive deep into “Research on Project-Based Teaching Methods in the Introduction to Artificial Intelligence” by Zhiqiang Hu, Zhongjin Guo, Shan Jiang, Xiaodong Zhao, and Xiaoqian Li from Taishan University, China. This study explores the effectiveness of project-based teaching methods in AI education and their impact on students’ academic performance, practical skills, and learning experience.

In this episode, we analyze the research paper “The Impact of Large Language Models on Programming Education and Student Learning Outcomes” by Gregor Jošt, Viktor Taneski, and Sašo Karakatič from the University of Maribor. Published in Applied Sciences (2024), this study explores how the use of AI models like ChatGPT and Copilot affects programming students’ performance, focusing on critical thinking, debugging skills...

How is Generative AI reshaping the labor market? In this episode, we break down the key findings from the paper “The Labor Market Effects of Generative Artificial Intelligence” by Jonathan S. Hartley (Stanford University), Filip Jolevski (World Bank), Vitor Melo (Clemson University), and Brendan Moore (Stanford University).

This Stanford & World Bank-backed study explores how Generati...

In this episode of SHIFTERLABS: The Future of Education, we explore the paper “Generative AI in Higher Education: A Global Perspective of Institutional Adoption Policies and Guidelines”, authored by Yueqiao Jin, Lixiang Yan, Vanessa Echeverria, Dragan Gašević, and Roberto Martinez-Maldonado from Monash University and Escuela Superior Politécnica del Litoral (Ecuador).

This study, publishe...

How is Generative AI revolutionizing English language education? In this episode, we dive into the latest research from Lucas Kohnke, Di Zou, and Fan Su (2025), published in Computers & Education: Artificial Intelligence. This study, conducted across universities in Hong Kong, China, explores how first-year university students use GenAI tools like ChatGPT, Grammarly, and Quillbot to enhance their writing, grammar,...