Link

https://motino101.itch.io/next 

Please play first.

Overview

I’ve been grappling with how quickly ChatGPT has integrated into our lives, especially considering it was only developed a year ago. It’s like having a second brain, not just in academics but in everyday scenarios. Recently, I had an argument with a friend, and their apology felt like it was written by ChatGPT. It was unsettling. This experience made me wonder what if ChatGPT became an internal part of our decision-making process? How would that reshape how we interact with the world and with ourselves? This is the core idea behind NexMind. I envisioned it set in a grungy, futuristic cityscape inspired by a neon-lit, foggy, and slightly retro-futuristic version of Hong Kong.

Game Concept

In a future where humanity has outsourced conscious thought to generative AI, only a select few, known as “retrievers,” can recover lost elements of human cognition. Players take on the role of one of these agents, a human whose mind is intertwined with a chatbot AI, providing constant guidance. As this character, you’re on a mission to retrieve a relic that could restore true human free thought.

Ideal Lesson

The game is designed to start reflection on how AI influences our decisions. It challenges players to consider that sometimes the machine’s “optimal” solution isn’t the right one. Can human instinct still lead us to the correct path? And what does it mean for human agency if we’re always guided by a recommendation engine?

Game Development History

Iteration 1: First Playtest

In the first iteration, I began with a broad storyline on a Canvas flowboard. The initial playtest was with Thanh, a 22-year-old CS major.

• Thanh found it hard to grasp the concept of “abstraction of thought,” which was how I initially described Nexmind. I rephrased it in the next version to communicate more simply.
• Thanh noted that the theme of empathy through saving the character Cipher’s sister resonated but felt too broad when placed alongside other larger contextual elements, like references to global superpowers. Based on her feedback, I decided to focus more on the sister’s rescue to sharpen the narrative and eliminate extraneous details that detracted from this focus.

Iteration 2: Refining Character

The next playtest was with Asher. 

• Asher found the original concept of the character as an “abstraction of human thought” unclear. Initially, I wanted the player to be an abstract being, but I shifted the design to a real human with AI integration.
• Asher also felt that the reason for saving Cipher’s sister felt disconnected. Therefore, I strengthened the character’s motivation by weaving in a backstory for the relic, as a failsafe created to override Nexmind.

Iteration 3: Better Context around choices

In the next playtest with Edwin, I had started creating the Twine prototype by mapping the Canva flowboard onto Twine.

• Edwin found several navigation bugs, which I fixed.
• Edwin highly complimented the “textured” feel of the game, describing how the sentence flow and varied fonts made him feel fully immersed in the environment.
• Edwin wanted more clarity around choices, such as the difference between entering “the archives” or “the network.” Based on this, I added brief descriptions to each choice, helping players make informed decisions about which path to pursue so the learnings about deciding between AI and human choices was more clear.
• Edwin also imagined the setting as a grungy, cyberpunk-inspired Hong Kong when I asked him, which aligned perfectly with the game’s intended aesthetic.

Iteration 4: Expanding pathways

After that, I playtested with Jason, a 21-year-old CS major Asian male.

• Jason played quickly and seemed to want to explore more paths and was interested in discovering what happens next. To build on this, I expanded the number of branched pathways.
• Jason was excited by the character’s identity, finding it slightly blurry yet compelling.
• Jason suggested that mission failure was acceptable to him, whereas I had imagined I wanted players to feel good by winning, so this inspired me to create more failures as part of the storyline.

Iteration 5: Adjusting pacing

I then playtested with AMY!

• Amy felt the gameplay moved too quickly, with some choices leading to abrupt endings. For instance, she died quickly after choosing a path. To address this, I paced the game to allow for more gradual tension-building, where players would experience “slow losses” rather than sudden game over.
• Amy appreciated the continuous textured experience and suggested adding even more detail to certain locations.

Iteration 6: adding more emotion vs. AI choices

In the playtest with Gaya, a CS major from Goa, India, I wanted to deepen the learning outcome of the game.

• Gaya noticed that many paths led to successful outcomes without significant setbacks. She suggested that each path should have distinct consequences, so I decided to introduce outcomes that included deaths and a clearer thought pattern about evaluating human vs. AI choices
• Gaya pointed out that two paths felt distinct—one emotional, the other objective. I started to develop these as separate game styles, representing one AI-driven and one human-driven path with varying likelihoods of success.
• Gaya felt that some choices still lacked meaningful context, which prompted me to provide brief explanations of each option. Additionally, I added minor “after-choice” descriptions that clarified the impact of each decision 

Iteration 7: as a stretch goal, experimenting with probability

A playtest with Zoe, a master’s student in education, and Pedro, another tester, led to some final thoughts.

• Both Zoe and Pedro suggested that incorporating a probability mechanic would add an interesting layer to the game; they both thought the percentages could be actually random and generated by the game. I may explore this in a future version due to bandwidth reasons, although I love this mechanic as it seems incredibly interesting to further the learning outcome of balancing randomness and chance with the tussle between human and AI choices.

Reflection

One of the biggest learnings I had was balancing playability with vibe. Initially, the concept of “abstracting thought” was too complex, but it conveyed the feeling I wanted to, of being disembodied and stateless, however playtests showed that much more relatable phrasing could better introduce the theme I wanted to. I also learned about shifting the focus from winning to LOSING – introducing branching paths and consequences gave choices weight, making every decision impactful. Finally, I learned about my strengths, which are especially creating texture. Players responded positively to the “textured” environments in the game with sensory details and varied fonts. My weaknesses of the game were that I focused too much on rounding out the story, but creating technical components for slowing the pacing of the game were needed.