Assessing student depression in sensitive environments like special education is challenging. Standardized questionnaires may not fully reflect students' true situations. Furthermore, automated methods often falter with rich student narratives, lacking the crucial, individualized insights stemming from teachers' empathetic connections with students. Existing methods often fail to address this ambiguity or effectively integrate educator understanding. To address these limitations by fostering a synergistic human-AI collaboration, this paper introduces Human Empathy as Encoder (HEAE), a novel, human-centered AI framework for transparent and socially responsible depression severity assessment. Our approach uniquely integrates student narrative text with a teacher-derived, 9-dimensional "Empathy Vector" (EV), its dimensions guided by the PHQ-9 framework,to explicitly translate tacit empathetic insight into a structured AI input enhancing rather than replacing human judgment. Rigorous experiments optimized the multimodal fusion, text representation, and classification architecture, achieving 82.74% accuracy for 7-level severity classification. This work demonstrates a path toward more responsible and ethical affective computing by structurally embedding human empathy

Empathy is increasingly recognized as a key factor in human-AI communication, yet conventional approaches to "digital empathy" often focus on simulating internal, human-like emotional states while overlooking the inherently subjective, contextual, and relational facets of empathy as perceived by users. In this work, we propose a human-centered taxonomy that emphasizes observable empathic behaviors and introduce a new dataset, Sense-7, of real-world conversations between information workers and Large Language Models (LLMs), which includes per-turn empathy annotations directly from the users, along with user characteristics, and contextual details, offering a more user-grounded representation of empathy. Analysis of 695 conversations from 109 participants reveals that empathy judgments are highly individualized, context-sensitive, and vulnerable to disruption when conversational continuity fails or user expectations go unmet. To promote further research, we provide a subset of 672 anonymized conversation and provide exploratory classification analysis, showing that an LLM-based classifier can recognize 5 levels of empathy with an encouraging average Spearman $ρ$=0.369 and Accuracy=0.487 over this set. Overall, our findings underscore the need for AI designs that dynamically tailor empathic behaviors to user contexts and goals, offering a roadmap for future research and practical development of socially attuned, human-centered artificial agents.

In 2015, a jovial three-foot-tall robot with pool noodles for arms set out on what seemed like a simple mission. Using the kindness of strangers, this machine, called "hitchBOT" would spend months hitchhiking across the continental United States. It made it just 300 miles. Two weeks into the road trip, HitchBOT was found abandoned in the streets of Philadelphia, its head severed and spaghetti arms ripped from its bucket-shaped body. "It was quite a setback, and we didn't really expect it," hitchBOT co-creator Frauke Zeller told CNN at the time.

Chris Wyper, Director of Global Industry Strategy, Retail and E-commerce at Talkdesk, explores how retailers can leverage AI solutions to achieve more personalised CX and stand out in a competitive market. The rise of personalised or hyper-personalised service is one of the most significant changes we've seen across the retail sector over recent years. It might seem a little ironic that, at a time when personal interactions in-store and on the phone have decreased, the process of making a purchase has actually become less transactional and more personalised. Digitisation has paved the way for retailers to gather more data and analytics. Not only has this digitisation helped retailers better understand their customers, it's enabled them to respond in real-time to meet their needs..

Emotional intelligence is a crucial aspect of customer communication, and key to deliver exceptional customer experiences. Today, leveraging automation in the sales, marketing and customer service arena while balancing the need for empathy is critical. While empathy is seen as a uniquely human trait, developments in artificial intelligence (AI) to help brands recognize and respond in an empathetic manner are on the rise and come at a critical time. Studies show that human empathy is on the decline – a deficit costing the average brand $300 million in lost revenue every year. For brands to truly connect with their customers to effectively market, sell and serve, they need to really understand the customer mindset. This is where empathic AI solutions are coming into the picture to complement and empower sales, marketing, and service professionals on the customer front lines to detect the customer's emotional state and intent and deliver a better customer experience.

At the heart of the development of AI appears to be a search for perfection. And it could be just as dangerous to humanity as the one that came from philosophical and pseudoscientific ideas of the 19th and early 20th centuries and led to the horrors of colonialism, world war and the Holocaust. Instead of a human ruling "master race", we could end up with a machine one. If this seems extreme, consider the anti-human perfectionism that is already central to the labour market. Here, AI technology is the next step in the premise of maximum productivity that replaced individual craftmanship with the factory production line.

Take this scenario: A customer calls your insurance hotline. Your digital employee, let's call her "Ivy," takes the call and authenticates the customer, asking them for their account number and PIN. Ivy also finds out why the customer is calling, without having to ask: through deep integration with your company's CRM and case management system, "she" sees the customer has an open claim with you and has just visited your website to review the case before calling. Ivy logically infers the customer is calling because they want to speak to a person about the claim, which Ivy quickly confirms before connecting the customer with a live person. The live agent, let's call him "Mark," now speaks with the customer and, through personal experience and intuition -- while also following business process and decision rules -- determines that to advance the claim, more information is necessary.