(Feature Impact) News of a death can spread online in seconds – often before families have notified close family members privately. That’s why grief experts urge people to rethink how they share condolences, tributes and loss-related information on social media, particularly during the winter months when grief can feel especially isolating.

“Grief etiquette is about putting the needs of the grieving family first, not our urge to say something publicly,” said Dr. Camelia L. Clarke, National Funeral Directors Association (NFDA) spokesperson, funeral director and grief educator with nearly 30 years of experience. “Just because information can be shared instantly doesn’t mean it should be.”

Social media has become a common place for sharing condolences, tributes and memories. However, grief experts caution that, without thoughtful consideration, online posts can unintentionally cause harm. Knowing when to post, what to say and when to remain silent can make a meaningful difference for families experiencing loss.

Consider this advice from the experts at the NFDA.

Grief Etiquette in the Digital Age

Grief etiquette refers to the unspoken guidelines for how individuals acknowledge death, loss and mourning, particularly online.

According to Clarke, one of the most important principles is restraint.

“When a death is shared online too quickly, families can feel exposed and overwhelmed at a moment when they’re still processing the loss themselves,” she said. “Waiting is an act of compassion.”

Best Practices for Posting About Loss Online

As social media continues to play a role in modern mourning, grief professionals encourage users to pause before posting and consider a few key guidelines:

• Let the family lead. Don’t post about a death until the immediate family has made it public.
• Ask permission. Obtain consent before sharing photos, stories or tributes.
• Reach out privately first. A direct message, call or handwritten note can be more meaningful than a public comment.
• Avoid speculation. Don’t ask about or share details regarding the cause of death.
• Offer ongoing support. Grief extends far beyond the first days or weeks after a loss.

What to Say (and Avoid)

When expressing condolences online, experts recommend simplicity, sincerity and sensitivity. Messages that acknowledge loss without attempting to explain or minimize it are often the most supportive.

Helpful phrases include:

• “I’m sorry for your loss.”
• “Thinking of you and your family.”
• “I’m here if you want to talk or need anything.”

By contrast, well-meaning cliches can unintentionally cause harm. Phrases such as “They’re in a better place” or “Everything happens for a reason” may reflect the speaker’s beliefs, but they can feel dismissive to someone grieving.

“Grieving people don’t need answers – they need presence,” Clarke said. “Listening matters more than saying the perfect thing.”

Resources for Families and Friends

As digital spaces continue to shape how people communicate during life’s most difficult moments, experts agree empathy, patience and respect remain timeless.

Advertisement

“Grief is deeply personal,” Clarke said. “When we slow down and lead with compassion, we honor both the person who has died and those who are left to grieve.”

To learn more about how to support a grieving person and access free, expert-reviewed resources for navigating grief, expressing condolences and supporting loved ones before, during and after a loss, visit RememberingALife.com, an initiative of the NFDA.

Photo courtesy of Shutterstock

    <img src="https://ssl.google-analytics.com/collect?v=1&amp;tid=UA-482330-7&amp;cid=1955551e-1975-5e52-0cdb-8516071094cd&amp;sc=start&amp;t=pageview&amp;dl=http%3A%2F%2Ftrack.familyfeatures.com%2F17832%2F10240&amp;dt=HOW-TO-PRACTICE-THOUGHTFUL-GRIEF-ETIQUETTE-ONLINE" />
<img src="https://erp.featureimpact.com/api/v1/tracking/17832/10240/track.gif" />

SOURCE:

National Funeral Directors Association

Last Updated on February 14, 2026 by Daily News Staff

Empowering Seniors for Safer Online Experiences: 6 Practical Safety Tips for Caregivers and Families

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.

Advertisement

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.

Advertisement

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.