I was halfway into my sustainable agriculture lecture at UC Santa Barbara on an otherwise pleasant February afternoon when I heard the sound no teacher wants to hear: one of my students, in the back row, snoring. Loudly. I decided to plow ahead, even as other students turned around and erupted into giggles. Finally, someone shook the offending student awake, and class proceeded.

Later that week, a teaching assistant approached me to explain how bad the snorer felt about the incident. It wasn’t that the student was uninterested or found my lecture boring, the TA explained; they just struggled to stay awake through such a passive and sedentary experience. It wasn’t the content of my class that was the problem. It was the format.

The longer I’ve taught (this is my 11th year as a professor), the more I’ve leaned on experiential learning: hands-on activities that get students out of their seats and engaging all their senses and capacities. Even as universities in my state are signing deals with tech companies to bring free AI training to campus, I see students clamoring for something else: meaningful in-person experiences where they can make strong connections with mentors and peers.

As I’ve redesigned my classes to integrate more field trips to local farms, volunteer work with community organizations and hands-on lessons focused on building tangible skills, I’ve found that students work harder, learn more, and look forward to class. Instead of just showing slides of compost, I bring my students to our campus farm to harvest castings (nutrient-dense worm poop!) from the worm bins. Instead of just lecturing about how California farmers are adapting to water scarcity, I take students to visit a farm that operates without irrigation, where we help prune and harvest grapes and olives. Long wait lists for these types of classes indicate that demand is far greater than supply.

I’m a proponent of experiential learning in almost every educational context, but there are several reasons why it is particularly relevant and essential this school year.

For one thing, generative AI has upended most traditional assignments. We can no longer assume that writing submitted by students is indicative of what they’ve learned. As many of my colleagues have found out the hard way, students are routinely completely unfamiliar with the content of their own papers. In this environment, there’s a real advantage to directly supervising and assessing students’ learning, rather than relying on proxies that robots can fake.

Second, today’s young adults face an uncertain economy and job market, partly due to AI. Many employers are deploying AI instead of hiring entry-level workers, or simply pausing hiring while waiting for markets to settle. As instructors, we must admit that we aren’t 100% sure which technical skills our students will need to succeed in this rapidly evolving workplace, especially five to 10 years down the road. Experiential learning has the advantage of helping students build the timeless, translatable skills that will AI-proof their employability: teamwork, communication, emotional intelligence and project management. As a bonus, community-engaged learning approaches can introduce students to professional settings in real time, ensuring a more up-to-date and relevant experience than any pre-cooked lesson plan.

Finally, and not unrelated to the above two points, Gen Z is experiencing a mental health crisis that inhibits many students’ ability to focus, set goals and develop self-confidence. There is nothing quite like putting a shovel and some seeds in their hands (preferably out of cellphone range) and watching them build a garden with their peers. The combined effect of being outdoors, digitally detoxing, moving about, bonding with others, and feeling a sense of accomplishment and making a difference is a powerful tonic for rumination and constant online isolation.

The field of environmental studies lends itself to outdoor experiential learning, and this has long been a key component of courses in ecology and earth science. But this approach can be quite powerful across the curriculum. I’ve known political science professors who take students to city council meetings, historians who walk students through the streets of their city to witness legacies of earlier eras, and writing instructors who bring groups of students to wild spaces to develop narrative essays on site.

With support from my department, I’m grateful to be able to teach an entirely experiential field course — but I’m equally excited about integrating modest experiential elements into my 216-person lecture course. Even one experiential assignment (like attending and reflecting on a public event) or hands-on activity in the discussion section can catalyze and deepen learning. 

To be sure, effective experiential learning is an art form that requires significant investment of time and energy from the instructor — and often from community partners as well. This work needs to be appropriately valued and compensated, and off-campus experiences require transportation funding and careful planning to ensure student safety. But the payoff can be the most meaningful and memorable experience of a student’s academic career. Instead of snoozing through a lecture, they can actively develop themselves into the adult they wish to become.

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Liz Carlisle is an associate professor of environmental studies at UC Santa Barbara and a Public Voices Fellow of the OpEd Project.

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Artificial intelligence is really revolutionizing the online gaming landscape by providing personalized solutions to consumers. Precision-based platforms are now personalizing gaming worlds based on personal choice without compromising regulatory mechanisms or consumer safety.

Machine learning innovations are really beginning to imprint virtual entertainment and gaming. Personalized recommendations, dynamic UI and risk management tools are commonplace in contemporary casino systems. Insights gleaned from user behavior are helping to optimize engagement plans, delivering safer and more enjoyable experiences.

The emergence of personalization through AI in internet gaming

Building sophisticated AI models has enabled a better understanding of behavior patterns. Sites examine a set of session lengths, game picks, stakes and timing of interaction to create individual profiles. Such profiles inform dynamic interfaces, which do not employ blanket templates. Interfaces are dynamic, presenting game offers of a comparable mechanic or styling preference in a non-intrusive manner. Suggestions can be variant slot themes, table game variants or even timing recommendations by peaks of historical activity. Such subtle refinements ensure that players are presented with experiences related to their interests, minimizing friction while navigating and providing variety. Implementing such models can increase user satisfaction within retention constraints appropriate for playing enjoyment and regulatory obligations, clearing the ground for even more innovative responsible gaming tools that adapt in sync with emerging behaviors.

Blending affordability with individualization

Price sensitivity indices are integrated into tailored systems. As a potential illustration, a website can identify frugal tendencies and thus emphasize low-risk headlines. This is especially true with minimum deposit online casinos, where minimum deposit limits are set low to facilitate access without compromise on disciplined usage. Artificial intelligence systems can subtly direct players towards options that allow spending, which may be monitored and sanction enjoyment in the same breath. Budget-sensitive audience groups can be given low-variance/volatility game lists, allowing for a customized experience mindful of their wallet and desire to play frequently. In the long term, this generates a sustainable cycle of engagement, where entertainment becomes a frequent, low-stakes activity rather than a rare, high-stakes event, such that affordability and personalization work in conjunction to construct a balanced virtual gaming sphere.

Increasing engagement via intelligent content distribution

Artificially intelligent content distribution systems respond on a discrete, granular basis to the preferences demonstrated by the user. Recommendations that refresh with shifts in behavior, such as increased use of evenings on live dealers or non-standard access on weekends for table games, cause refreshes in content viewing. Instead of static lists, casino lobbies offer individualized menus that are streamlined to focus on probable areas of interest. Display banners and graphic composition become less general, focusing instead on relevance and context. Such improvements make a better end-user experience possible, reducing time spent navigating and money spent interacting with games and better aligning individual taste profiles. In the long term, such responsiveness also helps identify subtle changes in engagement, allowing platforms to introduce new titles or features at points of most significant interest while gradually retiring less relevant ones, thereby producing a better, more dynamic relationship between platform design and user behavior.

Striking a balance with responsible play

AI systems are not merely crafted to engage optimally; there are plans for responsible gaming. Frequency, session length or increase in stakes deviating from patterns of regularity are tracked by detection algorithms, which raise alerts or nudges that cause self-awareness. When threshold levels are crossed, soft messages reminding of breaks taken or activity review are provided. Adaptive deposit limits can also be activated, where stakes are established based on past behavior and safety limits are defined. Focusing on player welfare averts personalization from turning into remorse-causing over-engagement and, on a related note, puts regulators’ minds at ease knowing safety is paramount. In the long term, such systems can evolve into forward-looking buddies, offering supporting tools like summaries of playing, spending dashboards or voluntary pauses, such that entertainment is complemented by accountability.

Regulatory and privacy concerns

Personalization elements must operate within a framework of data protection and compliance. Regulators examine player data usage, calling on platforms to clarify how behavioral analytics affect platform presentation. Platforms must include precise opt-out mechanisms for personalization elements and collection procedures must comply with privacy guidelines. Anonymization and data minimization are standard practices within the industry, ensuring that AI models learn patterns without holding personally identifiable information. Responsible vendors must utilize personalization algorithms to provide audit and transparency, demonstrating that the recommendations are not exploitative or unfairly nudging users towards higher spending.

Artificial intelligence-powered personalization represents a paradigm shift in video gaming, transitioning from static interfaces to dynamic, context-sensitive worlds. Personalized content streaming, dynamic recommendation and risk/benefit tailoring define a new norm of player-centric experience. The deployment of such technology fosters engagement that aligns with personal preferences and boundaries, strengthening a healthier bond between users and platforms.

Although AI introduces advanced opportunities for personalization, ongoing attention must focus on ethics, fairness and regulatory alignment. Transparency about algorithmic functionality, robust safeguards against over-entitlement and accessible user controls remain essential. Successful integration occurs when gaming platforms balance enjoyment, trust and safety, ensuring that personalization supports rather than undermines player well-being.

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