Lifestyle
Using AI to Write Valentine’s Day Notes Can Trigger Guilt — Here’s Why
Julian Givi, West Virginia University; Colleen P. Kirk, New York Institute of Technology, and Danielle Hass, West Virginia University
Whether it’s Valentine’s Day notes or emails to loved ones, using AI to write leaves people feeling crummy about themselves
As Valentine’s Day approaches, finding the perfect words to express your feelings for that special someone can seem like a daunting task – so much so that you may feel tempted to ask ChatGPT for an assist.
After all, within seconds it can dash off a well-written, romantic message. Even a short, personalized limerick or poem is no sweat.
But before you copy and paste that AI-generated love note, you might want to consider how it could make you feel about yourself.
We research the intersection of consumer behavior and technology, and we’ve been studying how people feel after using generative AI to write heartfelt messages. It turns out that there’s a psychological cost to using the technology as your personal ghostwriter.
The rise of the AI ghostwriter
Generative AI has transformed how many people communicate. From drafting work emails to composing social media posts, these tools have become everyday writing assistants. So it’s no wonder some people are turning to them for more personal matters, too.
Wedding vows, birthday wishes, thank you notes and even Valentine’s Day messages are increasingly being outsourced to algorithms.
The technology is certainly capable. Chatbots can craft emotionally resonant responses that sound genuinely heartfelt.
But there’s a catch: When you present these words as your own, something doesn’t sit right.
When convenience breeds guilt
We conducted five experiments with hundreds of participants, asking them to imagine using generative AI to write various emotional messages to loved ones. Across every scenario we tested – from appreciation emails to birthday cards to love letters – we found the same pattern: People felt guilty when they used generative AI to write these messages compared to when they wrote the messages themselves.
When you copy an AI-generated message and sign your name to it, you’re essentially taking credit for words you didn’t write.
This creates what we call a “source-credit discrepancy,” which is a gap between who actually created the message and who appears to have created it. You can see these discrepancies in other contexts, whether it’s celebrity social media posts written by public relations teams or political speeches composed by professional speechwriters.
When you use AI, even though you might tell yourself you’re just being efficient, you can probably recognize, deep down, that you’re misleading the recipient about the personal effort and thought that went into the message.
The transparency test
To better understand this guilt, we compared AI-generated messages to other scenarios. When people bought greeting cards with preprinted messages, they felt no guilt at all. This is because greeting cards are transparently not written by you. Greeting cards carry no deception: Everyone understands you selected the card and that you didn’t write it yourself.
We also tested another scenario: having a friend secretly write the message for you. This produced just as much guilt as using generative AI. Whether the ghostwriter is human or an artificial intelligence tool doesn’t matter. What matters most is the dishonesty.
There were some boundaries, however. We found that guilt decreased when messages were never delivered and when recipients were mere acquaintances rather than close friends.
These findings confirm that the guilt stems from violating expectations of honesty in relationships where emotional authenticity matters most.
Somewhat relatedly, research has found that people react more negatively when they learn a company used AI instead of a human to write a message to them.
But the backlash was strongest when audiences expected personal effort – a boss expressing sympathy after a tragedy, or a note sent to all staff members celebrating a colleague’s recovery from a health scare. It was far weaker for purely factual or instructional notes, such as announcing routine personnel changes or providing basic business updates.
What this means for your Valentine’s Day
So, what should you do about that looming Valentine’s Day message? Our research suggests that the human hand behind a meaningful message can help both the writer and the recipient feel better.
This doesn’t mean you can’t use generative AI as a brainstorming partner rather than a ghostwriter. Let it help you overcome writer’s block or suggest ideas, but make the final message truly yours. Edit, personalize and add details that only you would know. The key is co-creation, not complete delegation.
Generative AI is a powerful tool, but it’s also created a raft of ethical dilemmas, whether it’s in the classroom or in romantic relationships. As these technologies become more integrated into everyday life, people will need to decide where to draw the line between helpful assistance and emotional outsourcing.
This Valentine’s Day, your heart and your conscience might thank you for keeping your message genuinely your own.
Julian Givi, Assistant Professor of Marketing, West Virginia University; Colleen P. Kirk, Assistant Professor of Marketing, New York Institute of Technology, and Danielle Hass, Ph.D. Candidate in Marketing, West Virginia University
This article is republished from The Conversation under a Creative Commons license. Read the original article.
Our Lifestyle section on STM Daily News is a hub of inspiration and practical information, offering a range of articles that touch on various aspects of daily life. From tips on family finances to guides for maintaining health and wellness, we strive to empower our readers with knowledge and resources to enhance their lifestyles. Whether you’re seeking outdoor activity ideas, fashion trends, or travel recommendations, our lifestyle section has got you covered. Visit us today at https://stmdailynews.com/category/lifestyle/ and embark on a journey of discovery and self-improvement.
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Tech
When ‘Head in the Clouds’ Means Staying Ahead
Head in the Clouds: Cloud is no longer just storage—it’s the intelligent core of modern business. Explore how “cognitive cloud” blends AI and cloud infrastructure to enable real-time, self-optimizing operations, improve customer experiences, and accelerate enterprise modernization.
Last Updated on February 7, 2026 by Daily News Staff
When ‘Head in the Clouds’ Means Staying Ahead
(Family Features) You approve a mortgage in minutes, your medical claim is processed without a phone call and an order that left the warehouse this morning lands at your door by dinner. These moments define the rhythm of an economy powered by intelligent cloud infrastructure. Once seen as remote storage, the cloud has become the operational core where data, AI models and autonomous systems converge to make business faster, safer and more human. In this new reality, the smartest companies aren’t looking up to the cloud; they’re operating within it. Public cloud spending is projected to reach $723 billion in 2025, according to Gartner research, reflecting a 21% increase year over year. At the same time, 90% of organizations are expected to adopt hybrid cloud by 2027. As cloud becomes the universal infrastructure for enterprise operations, the systems being built today aren’t just hosted in the cloud, they’re learning from it and adapting to it. Any cloud strategy that doesn’t account for AI workloads as native risks falling behind, holding the business back from delivering the experiences consumers rely on every day. After more than a decade of experimentation, most enterprises are still only partway up the curve. Based on Cognizant’s experience, roughly 1 in 5 enterprise workloads has moved to the cloud, while many of the most critical, including core banking, health care claims and enterprise resource planning, remain tied to legacy systems. These older environments were never designed for the scale or intelligence the modern economy demands. The next wave of progress – AI-driven products, predictive operations and autonomous decision-making – depends on cloud architectures designed to support intelligence natively. This means cloud and AI will advance together or not at all.The Cognitive Cloud: Cloud and AI as One System
For years, many organizations treated migration as a finish line. Applications were lifted and shifted into the cloud with little redesign, trading one set of constraints for another. The result, in many cases, has been higher costs, fragmented data and limited room for innovation. “Cognitive cloud” represents a new phase of evolution. Imagine every process, from customer service to supply-chain management, powered by AI models that learn, reason and act within secure cloud environments. These systems store and interpret data, detect patterns, anticipate demand and automate decisions at a scale humans simply cannot match. In this architecture, AI and cloud operate in concert. The cloud provides computing power, scale and governance while AI adds autonomy, context and insight. Together, they form an integrated platform where cloud foundations and AI intelligence combine to enable collaboration between people and systems. This marks the rise of the responsive enterprise; one that senses change, adjusts instantly and builds trust through reliability. Cognitive cloud platforms combine data fabric, observability, FinOps and SecOps into an intelligent core that regulates itself in real time. The result is invisible to consumers but felt in every interaction: fewer errors, faster responses and consistent experiences.Consumer Impact is Growing
The impact of cognitive cloud is already visible. In health care, 65% of U.S. insurance claims run through modernized, cloud-enabled platforms designed to reduce errors and speed up reimbursement. In the life sciences industry, a pharmaceuticals and diagnostics firm used cloud-native automation to increase clinical trial investigations by 20%, helping get treatments to patients sooner. In food service, intelligent cloud systems have reduced peak staffing needs by 35%, in part through real-time demand forecasting and automated kitchen operation. In insurance, modernization has produced multi-million-dollar savings and faster policy issuance, improving both customer experience and financial performance. Beneath these outcomes is the same principle: architecture that learns and responds in real time. AI-driven cloud systems process vast volumes of data, identify patterns as they emerge and automate routines so people can focus on innovation, care and service. For businesses, this means fewer bottlenecks and more predictive operations. For consumers, it means smarter, faster, more reliable services, quietly shaping everyday life. While cloud engineering and AI disciplines remain distinct, their outcomes are increasingly intertwined. The most advanced architectures now treat intelligence and infrastructure as complementary forces, each amplifying the other.Looking Ahead
This transformation is already underway. Self-correcting systems predict disruptions before they happen, AI models adapt to market shifts in real time and operations learn from every transaction. The organizations mastering this convergence are quietly redefining themselves and the competitive landscape. Cloud and AI have become interdependent priorities within a shared ecosystem that moves data, decisions and experiences at the speed customers expect. Companies that modernize around this reality and treat intelligence as infrastructure will likely be empowered to reinvent continuously. Those that don’t may spend more time maintaining the systems of yesterday than building the businesses of tomorrow. Learn more at cognizant.com. Photo courtesy of ShutterstockCulver’s Thank You Farmers® Project Hits $8 Million Donation MilestoneLink: https://stmdailynews.com/culvers-thank-you-farmers-project-hits-8-million-donation-milestone/
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love and romance
Love Your Space: 4 Valentine’s Day Home Decor Ideas
Valentine’s Day offers an opportunity to enhance home decor with love-themed touches. Key ideas include using a classic red and pink palette, incorporating soft lighting and inviting textures, adding fresh flowers and heartfelt accents, and personalizing decor with meaningful items. Each element contributes to a romantic and welcoming atmosphere.
Love Your Space: 4 Valentine’s Day Home Decor Ideas
(Family Features) From planning a romantic night in with your significant other to hosting friends for Galentine’s Day, Valentine’s Day is a perfect opportunity to fill your home with love and heartfelt style.
Whether you add subtle accents or bold pops of color, decorating for the season of love is about adding intentional touches that make your spaces feel special.
1. Choose a Valentine’s Palette
The classic red and pink motif is a perfect starting point. A few heart-shaped throw pillows, blush pink accessories or a rich red accent blanket can capture the spirit without overwhelming. If bold colors don’t match your current design style, ground them with neutrals like soft whites, creams or grays to create a romantic look that feels intentional and cohesive.
2. Set the Mood with Lighting and Texture
Soft lighting – think string lights draped along a mantel, clusters of warm-hued candles or a table lamp with a rosy glow – can make rooms feel cozier, as can layering sensual textures like velvet pillows, knit throws and lace or crochet accents. These elements feel inviting and chic, creating a relaxed, intimate ambience perfect for a celebratory evening at home.
3. Fresh Florals and Heartfelt Accents
A timeless Valentine’s Day tradition, fresh flowers can bring life, color and fragrance to any room. A vase of red roses, pink tulips or mixed seasonal blooms can serve as a centerpiece on your dining room table or entry console. For an added seasonal touch, consider heart-shaped garlands or DIY paper hearts on shelves, mirrors or around picture frames.
4. Personalize With Love
Much like heart-warming gifts, the most meaningful decor often has a personal story. Frame a favorite photo, display a handwritten love note or incorporate a treasured keepsake into your Valentine’s arrangement to make your space feel uniquely yours.
For more ideas to celebrate love every time you walk through the door, visit eLivingtoday.com.
Photo courtesy of Shutterstock
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child education
Special Education Is Turning to AI to Fill Staffing Gaps—But Privacy and Bias Risks Remain
With special education staffing shortages worsening, schools are using AI to draft IEPs, support training, and assist assessments. Experts warn the benefits come with major risks—privacy, bias, and trust.
Seth King, University of Iowa
In special education in the U.S., funding is scarce and personnel shortages are pervasive, leaving many school districts struggling to hire qualified and willing practitioners.
Amid these long-standing challenges, there is rising interest in using artificial intelligence tools to help close some of the gaps that districts currently face and lower labor costs.
Over 7 million children receive federally funded entitlements under the Individuals with Disabilities Education Act, which guarantees students access to instruction tailored to their unique physical and psychological needs, as well as legal processes that allow families to negotiate support. Special education involves a range of professionals, including rehabilitation specialists, speech-language pathologists and classroom teaching assistants. But these specialists are in short supply, despite the proven need for their services.
As an associate professor in special education who works with AI, I see its potential and its pitfalls. While AI systems may be able to reduce administrative burdens, deliver expert guidance and help overwhelmed professionals manage their caseloads, they can also present ethical challenges – ranging from machine bias to broader issues of trust in automated systems. They also risk amplifying existing problems with how special ed services are delivered.
Yet some in the field are opting to test out AI tools, rather than waiting for a perfect solution.
A faster IEP, but how individualized?
AI is already shaping special education planning, personnel preparation and assessment.
One example is the individualized education program, or IEP, the primary instrument for guiding which services a child receives. An IEP draws on a range of assessments and other data to describe a child’s strengths, determine their needs and set measurable goals. Every part of this process depends on trained professionals.
But persistent workforce shortages mean districts often struggle to complete assessments, update plans and integrate input from parents. Most districts develop IEPs using software that requires practitioners to choose from a generalized set of rote responses or options, leading to a level of standardization that can fail to meet a child’s true individual needs.
Preliminary research has shown that large language models such as ChatGPT can be adept at generating key special education documents such as IEPs by drawing on multiple data sources, including information from students and families. Chatbots that can quickly craft IEPs could potentially help special education practitioners better meet the needs of individual children and their families. Some professional organizations in special education have even encouraged educators to use AI for documents such as lesson plans.
Training and diagnosing disabilities
There is also potential for AI systems to help support professional training and development. My own work on personnel development combines several AI applications with virtual reality to enable practitioners to rehearse instructional routines before working directly with children. Here, AI can function as a practical extension of existing training models, offering repeated practice and structured support in ways that are difficult to sustain with limited personnel.
Some districts have begun using AI for assessments, which can involve a range of academic, cognitive and medical evaluations. AI applications that pair automatic speech recognition and language processing are now being employed in computer-mediated oral reading assessments to score tests of student reading ability.
Practitioners often struggle to make sense of the volume of data that schools collect. AI-driven machine learning tools also can help here, by identifying patterns that may not be immediately visible to educators for evaluation or instructional decision-making. Such support may be especially useful in diagnosing disabilities such as autism or learning disabilities, where masking, variable presentation and incomplete histories can make interpretation difficult. My ongoing research shows that current AI can make predictions based on data likely to be available in some districts.
Privacy and trust concerns
There are serious ethical – and practical – questions about these AI-supported interventions, ranging from risks to students’ privacy to machine bias and deeper issues tied to family trust. Some hinge on the question of whether or not AI systems can deliver services that truly comply with existing law.
The Individuals with Disabilities Education Act requires nondiscriminatory methods of evaluating disabilities to avoid inappropriately identifying students for services or neglecting to serve those who qualify. And the Family Educational Rights and Privacy Act explicitly protects students’ data privacy and the rights of parents to access and hold their children’s data.
What happens if an AI system uses biased data or methods to generate a recommendation for a child? What if a child’s data is misused or leaked by an AI system? Using AI systems to perform some of the functions described above puts families in a position where they are expected to put their faith not only in their school district and its special education personnel, but also in commercial AI systems, the inner workings of which are largely inscrutable.
These ethical qualms are hardly unique to special ed; many have been raised in other fields and addressed by early-adopters. For example, while automatic speech recognition, or ASR, systems have struggled to accurately assess accented English, many vendors now train their systems to accommodate specific ethnic and regional accents.
But ongoing research work suggests that some ASR systems are limited in their capacity to accommodate speech differences associated with disabilities, account for classroom noise, and distinguish between different voices. While these issues may be addressed through technical improvement in the future, they are consequential at present.
Embedded bias
At first glance, machine learning models might appear to improve on traditional clinical decision-making. Yet AI models must be trained on existing data, meaning their decisions may continue to reflect long-standing biases in how disabilities have been identified.
Indeed, research has shown that AI systems are routinely hobbled by biases within both training data and system design. AI models can also introduce new biases, either by missing subtle information revealed during in-person evaluations or by overrepresenting characteristics of groups included in the training data.
Such concerns, defenders might argue, are addressed by safeguards already embedded in federal law. Families have considerable latitude in what they agree to, and can opt for alternatives, provided they are aware they can direct the IEP process.
By a similar token, using AI tools to build IEPs or lessons may seem like an obvious improvement over underdeveloped or perfunctory plans. Yet true individualization would require feeding protected data into large language models, which could violate privacy regulations. And while AI applications can readily produce better-looking IEPs and other paperwork, this does not necessarily result in improved services.
Filling the gap
Indeed, it is not yet clear whether AI provides a standard of care equivalent to the high-quality, conventional treatment to which children with disabilities are entitled under federal law.
The Supreme Court in 2017 rejected the notion that the Individuals with Disabilities Education Act merely entitles students to trivial, “de minimis” progress, which weakens one of the primary rationales for pursuing AI – that it can meet a minimum standard of care and practice. And since AI really has not been empirically evaluated at scale, it has not been proved that it adequately meets the low bar of simply improving beyond the flawed status quo.
But this does not change the reality of limited resources. For better or worse, AI is already being used to fill the gap between what the law requires and what the system actually provides.
Seth King, Associate Profess of Special Education, University of Iowa
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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