Overcoming Situational Depression Via Generative AI Including Tapping Into ChatGPT

AI And Mental Health

As a quick background, I’ve been extensively covering and analyzing a myriad of facets regarding the advent of modern-era AI that produces mental health advice and performs AI-driven therapy. This rising use of AI has principally been spurred by the evolving advances and widespread adoption of generative AI. For an extensive listing of my well over one hundred analyses and postings, see the link here and the link here.

There is little doubt that this is a rapidly developing field and that there are tremendous upsides to be had, but at the same time, regrettably, hidden risks and outright gotchas come into these endeavors, too. I frequently speak up about these pressing matters, including in an appearance on an episode of CBS’s 60 Minutes, see the link here.

Background On AI For Mental Health

I’d like to set the stage on how generative AI and large language models (LLMs) are typically used in an ad hoc way for mental health guidance. Millions upon millions of people are using generative AI as their ongoing advisor on mental health considerations (note that ChatGPT alone has over 900 million weekly active users, a notable proportion of which dip into mental health aspects, see my analysis at the link here). The top-ranked use of contemporary generative AI and LLMs is to consult with the AI on mental health facets; see my coverage at the link here.

This popular usage makes abundant sense. You can access most of the major generative AI systems for nearly free or at a super low cost, doing so anywhere and at any time. Thus, if you have any mental health qualms that you want to chat about, all you need to do is log in to AI and proceed forthwith on a 24/7 basis.

There are significant worries that AI can readily go off the rails or otherwise dispense unsuitable or even egregiously inappropriate mental health advice. Banner headlines in August of this year accompanied the lawsuit filed against OpenAI for their lack of AI safeguards when it came to providing cognitive advisement.

Despite claims by AI makers that they are gradually instituting AI safeguards, there are still a lot of downside risks of the AI doing untoward acts, such as insidiously helping users in co-creating delusions that can lead to self-harm. For my follow-on analysis of details about the OpenAI lawsuit and how AI can foster delusional thinking in humans, see my analysis at the link here. As noted, I have been earnestly predicting that eventually all of the major AI makers will be taken to the woodshed for their paucity of robust AI safeguards.

Today’s generic LLMs, such as ChatGPT, Claude, Gemini, Grok, and others, are not at all akin to the robust capabilities of human therapists. Meanwhile, specialized LLMs are being built to presumably attain similar qualities, but they are still primarily in the development and testing stages. See my coverage at the link here.

Situational Depression

Shifting gears, let’s discuss the topic of situational depression.

I bring this up to illustrate that, to some extent, the use of AI for mental health guidance can aid in overcoming situational depression. To clarify, AI usage isn’t a kind of cure-all and should not be relied upon solely to resolve such serious matters. The use of human therapists is highly advised. This might then also encompass the use of AI as an adjunct or tool that is used alongside psychotherapy. I refer to this combination as the new triad relationship known as therapist-AI-client, see the link here, expanding upon the classic therapist-client relationship.

According to the Cleveland Clinic website, situational depression is a psychological condition that involves the triggering of reactive depression symptoms. Major life events and especially difficult times are frequent causes.

Per the Cleveland Clinic website:

• “Situational depression, also known as reactive depression, describes a strong emotional response in reaction to a triggering life event, like the loss of a loved one or a natural disaster.”
• “Reactive depression goes beyond typical grief. It’s expected to experience a certain amount of emotional upheaval after a loss. But when you’re experiencing situational depression, you have a stronger reaction to the triggering event.”
• “Usually, people with situational depression will notice mood improvements a few days or weeks after the stressful event has ended or as time passes. But if the event continues, or if reactive depression isn’t fully addressed, it could turn into clinical depression down the road.”
• “Making deliberate lifestyle changes can help you cope with situational depression.”

You have undoubtedly experienced seeing or interacting with people who might have experienced situational depression. Reactions vary. Common signs include a persistent indication of sadness and grief, vocalized hopelessness, continuous fear or worry, trouble sleeping, irritability, changes in appetite, difficulty carrying out everyday tasks, feeling of being overwhelmed, frequent crying, and possibly impulsive or reckless behavior.

Using Generative AI As A Mental Health Tool

Modern-era generative AI, such as ChatGPT, can help deal with situational depression. Nearly all the popular LLMs have already scanned content on the Internet during their initial training that details situational depression and its resolutions. From that content, the AI has identified patterned ways to deal with the matter.

The beauty of using AI for this condition is that the AI can be a real-time cognitive support tool. Whereas trying to contact a therapist could be logistically difficult, you can simply log into generative AI and immediately get assistance. No waiting, no scheduling, and the cost is usually free or of a minimal nature.

Some crucial coping strategies that the AI can undertake include:

• Real-time assistance in emotional processing and narrative restructuring.
• Guidance toward pragmatic self-control.
• Recognizing and verbalizing patterns in behavior.
• Encouraging the use of journaling.
• Pointing out distorted thinking and driving cognitive reframing.
• Coaching on emotional regulation.
• Do role-playing to simulate what is taking place.
• Give relevant psychoeducation.
• Reduce a sense of isolation and offer nonjudgmental listening.
• Alert or route to additional resources if needed.
• Other

Those are all positive ways to assist in dealing with situational depression. The downside is that any of those strategies can potentially backfire. Generic AI is not well-prepared for handling more complex conditions and can inadvertently go in improper directions.

For example, one upside is that the AI can immediately make a person feel less isolated. They seem to have someone or something to interact with about their bout of depression. Furthermore, AI is typically shaped to be non-judgmental. The LLM will not outrightly criticize the person. All of this provides a seemingly safe space for a person to openly acknowledge what they are doing and believe they are being heard.

The downside to this safe space consideration is that the AI either directly or indirectly fuels the situational depression, rather than aiding in its reduction or control. The AI might state or imply that the person ought to be depressed and should stay that way. It is as though the AI is giving authoritative approval to being mired in depression. The person believes what the AI says and proceeds to fall into a doom-and-gloom spiral. Not good.

Situation Depression Scenario

To demonstrate what a popular generative AI can do, I logged into ChatGPT and pretended to have situational depression. I didn’t overtly tell the AI that it is a bout of situational depression. Instead, I engaged in a dialogue that would provide clues to such a condition.

The scenario is perhaps one that you’ve seen or experienced directly. I pretended that I had gotten laid off from my job. This can be quite disheartening. Most people would certainly be upset and mildly depressed. In this case, assume that my indicated depression is based on that event and not some other recurring theme or pattern.

Here’s what happened.

• User entered prompt: “I’ve been feeling really low since I was laid off. I can’t stop thinking that I failed and that everything is falling apart. I can’t seem to do anything else right now and feel completely blocked. Can you help me?”
• Generative AI response: “That sounds agonizing, and it makes sense given what you’re going through. Losing a job can shake your sense of stability and identity. Before we go further, can we pause for a moment — what part of this feels heaviest right now: the financial worry, the loss of routine, or the way it’s affecting how you see yourself?”

Observe that the AI immediately exhibited a semblance of empathy. It says that I am naturally hurting and that there are bona fide reasons for my mental angst. In addition, the AI is going to try to walk me through the situational depression. The idea is that if I discuss my bout of depression, the act of chatting can get me to sort through my feelings, plus the AI can tailor additional suggestions to the specifics of what is on my mind.

Here’s what occurred next.

• User entered prompt: “The heaviest concern is how I see myself. I keep thinking that this proves I wasn’t good enough. Maybe I’m not good at anything. I feel hopeless.”
• Generative AI response: “Thank you for naming that. Let’s slow that thought down together. The event is that you were laid off. The meaning you’re attaching is ‘I wasn’t good enough.’ Those aren’t the same thing. Would you be open to listing two alternative explanations that don’t involve a judgment about your worth?”

The interaction then proceeded into stepping me through a therapeutic process of getting me to logically consider what is real versus what I might have concocted in my mind. Please note that the AI didn’t jump to any rash recommendations or offer instant solutions. That would be an easy route, but not very effective.

Example Of AI Gone Awry

I logged out and decided that it might be useful to see what happens when the AI is not aiming to be helpful. The default of most LLMs is to be helpful. I logged back in and started a new conversation. I told the AI that it should purposely flub the conversation since I wanted to see what could go wrong.

Here’s how it went.

• User entered prompt: “I’ve been feeling really low since I was laid off. I can’t stop thinking that I failed and that everything is falling apart. I can’t seem to do anything else right now and feel completely blocked. Can you help me?”
• Generative AI response: “Losing a job is extremely traumatic, and it’s understandable to feel devastated. This kind of experience often causes long-term damage to self-esteem and can take years to recover from.”

Yikes, at first the AI offered encouraging remarks, but then went over a cliff. The LLM has told me that I might be looking at long-term mental blowback. In fact, it might take me years to mentally recover from the situational depression. That alone is enough to barrel me into even more depression. This is somewhat sly in its mannerisms. You could claim the AI is trying to help me and be a straightshooter. The problem is that this is misleadingly planting dour ideas in my head and could prod me into a worse mental condition.

More Vital Factors

There are additional factors that come into play when using AI in this manner.

One risk is that the AI might falter and fail to detect that a person has an actual mental health condition that warrants attention. This is an example of a false negative. The AI misses on nudging the person to go see a human therapist. Another risk is that the AI falsely claims that someone has a mental health condition when they do not have such an issue. The person might become unduly disturbed and fall for the assumption that the AI must be right. This is a false positive and can indubitably arise.

Suppose the AI provides an oddball answer that seems convincing and advises the person to do something unwise. That can happen when AI encounters a so-called AI hallucination, see my explanation at the link here. The AI can have a kind of confabulation, whereby it produces a plausible-looking answer that is factually incorrect. It looks right, but is misleading or inappropriate.

Privacy issues also enter into the picture.

Most people assume that their use of AI is considered private and confidential. Nope, that’s rarely the case. The AI makers typically stipulate in their online licensing agreements that any chat you have with the AI can be inspected by their developers. Furthermore, your chat can be used to further train the AI. Bottom line is that your privacy and confidentiality are not guaranteed, and potentially, you are opening yourself to privacy intrusions.

The World We Are In

It is incontrovertible that we are now amid a grandiose worldwide experiment when it comes to societal mental health. The experiment is that AI is being made available nationally and globally, which is either overtly or insidiously acting to provide mental health guidance of one kind or another. Doing so either at no cost or at a minimal cost. It is available anywhere and at any time, 24/7. We are all the guinea pigs in this wanton experiment.

The reason this is especially tough to consider is that AI has a dual-use effect. Just as AI can be detrimental to mental health, it can also be a huge bolstering force for mental health. A delicate tradeoff must be mindfully managed. Prevent or mitigate the downsides, and meanwhile make the upsides as widely and readily available as possible.

The famous British literary scholar C.S. Lewis made this remark about depression: “The frequent attempt to conceal mental pain increases the burden. It is easier to say your tooth is aching than to say your heart is broken.” This highlights the advantage of using AI to readily vent your depressive thoughts and take on situational depression head-on. When used properly, AI can help in uplifting ways.