If Gardenscapes Levels Are Tested, Why Are Some So Frustrating?

Frustrated Austin testing a Gardenscapes puzzle beside a cheerful AI robot, while a monitor behind them displays a green approval checkmark.

Gardenscapes levels can pass testing and still feel frustrating. Discover why AI scores cannot fully capture clarity, satisfaction, or player experience.

A level can be beatable, have the intended difficulty, and run without technical problems. Players can still find it exhausting, confusing, or dull. That is where evaluation gets harder: how do you decide whether a level is actually worth playing?

For AI, evaluating a level poses a different challenge than creating a playable board. A system might examine whether objectives can be completed, how the board behaves, or how well different actions work. Those findings depend on the criteria it has been given, and they do not tell the whole story of what a person understands or feels while playing.

The question, then, is whether an AI evaluation is enough to make the final decision. In a Gardenscapes level, difficulty, clarity, and player satisfaction do not always line up.

First, Someone Has to Decide What Makes a “Good Level”

Before a system can score a level, someone has to define what the design is supposed to accomplish.

A level might introduce an element, require a carefully planned sequence of actions, or give players a breather between harder boards. Judging all of them by exactly the same standard would make little sense.

If the goal is to teach a mechanic, a quick win can be a positive sign. If the goal is for players to discover a particular interaction, that same quick win might mean they skipped the part that gives the puzzle its purpose.

AI can work with different goals, provided those goals are clearly defined. But asking it to find the “best” level leaves an essential question unanswered: best at what? That is a design decision.

A Computer Can Find the Solution Without Showing Whether Players Will Understand It

Imagine a hypothetical level in which a generator must be activated before players can make progress toward the main objective.

An automated player might consistently choose the right action. That shows the approach works within the rules of the game. It does not necessarily show that someone seeing the level for the first time understands why they should start there.

A real player might notice another object first, misread a visual cue, or not yet know how the generator works. Their difficulty begins with understanding the board, before they can even start choosing the right moves.

An AI model could be designed to simulate some of these behaviors. The question would then be how closely that simulation matches real players. An agent beating the level does not automatically prove that the level makes its logic clear.

A Required Action Means Different Things in Different Levels

In a hypothetical introductory level, the designer might want players to take one specific action first. They discover how a new element works and immediately see the result.

Limited choices can help in that situation. Players can focus on the new rule without sorting through a dozen unrelated possibilities.

The same requirement in a later level can have a very different effect. If the necessary action is unclear and every other choice leads to failure, players may feel they are being punished for failing to guess the solution.

Data showing that nearly every successful attempt begins with the same move cannot settle that question. What have players already learned? What can they see on the board? Is the required move a clear lesson or a hidden trap?

Finding a Shortcut Does Not Always Mean the Design Should Change

Suppose the designer expects players to open the center of the board first. During evaluation, a more effective route appears along the edge.

That discovery needs a closer look. If the alternative route gives players an interesting choice, it may be worth keeping. If it lets them finish without ever using the mechanic the level is supposed to introduce, an adjustment may be needed.

A faster solution is not, on its own, a reason to change the design. What matters is whether it strengthens the puzzle or sidesteps its purpose.

AI can help find that route. The designer then has to decide whether the discovery belongs in the finished level.

Familiarity With Old Levels Can Limit the Evaluation of a New One

A system that compares a new level with previous ones has a particular frame of reference. That can be useful when the board resembles situations it has already examined.

An unusual layout or a new interaction makes the task more demanding. A level should not be labeled problematic simply because it looks different from earlier examples.

This is a potential limitation of that approach. It does not mean every AI system rejects original ideas. The result depends on how the system was trained and what it has been asked to evaluate.

A negative result might point to a specific unwanted behavior, or it might reflect a lack of comparable examples. The first calls for investigating the behavior. The second calls for questioning how confidently the system can judge something unfamiliar.

The Result of an Attempt Does Not Tell the Whole Story

Two players can finish the same level using the same number of moves and have completely different experiences.

One might gradually figure out the solution and feel that their decisions earned the win. The other might play without a clear plan until a chain reaction takes care of the objectives.

The final result does not capture that difference. A failed attempt is equally open to interpretation. Does the player want another try because they now know what to change? Or are they ready to quit because they still have no idea what happened?

A fuller evaluation needs evidence from the human experience, including observing real players and gathering carefully framed feedback. That evidence also needs interpretation: different people will not necessarily respond to the same level in the same way.

Where a Level Appears Can Change How It Feels

A demanding level does not exist in a vacuum. Players arrive after completing other boards, learning particular mechanics, and investing time and resources.

A level that feels like an interesting challenge after a few easier boards can become exhausting after a long stretch of difficult attempts.

An evaluation focused entirely on the individual board can miss that context. A broader model could include it, but someone would still need to check whether the model adequately represents the players’ journey.

A board can work well on its own and still be a poor fit for that particular point in the game.

What the Research Shows—and What Is a Design Interpretation

The study Improving Conditional Level Generation Using Automated Validation in Match-3 Games examines layout generation using characteristics such as difficulty, size, and symmetry. Its results support the use of specific measurements to improve the validity of generated levels.

The study Towards a Generic Method of Evaluating Game Levels proposes measurable criteria for concepts such as exploration and balance, with applications to strategy game maps and roguelike dungeons. It is not a Gardenscapes-specific study.

These papers show how selected aspects of design can be turned into evaluation criteria. They do not establish that an overall score fully captures a person’s experience.

The Gardenscapes examples in this article are hypothetical. They illustrate the questions a designer might ask when interpreting an evaluation. They do not describe a confirmed Playrix evaluation system.

Designers Need Explanations, Not Just Scores

A result such as “low quality” leaves too many questions unanswered. Which criterion was not met? Under what conditions? For which players?

A useful evaluation would describe the finding and its limits. For example: successful attempts depend heavily on one opening action, but whether new players understand that it is necessary has not been examined.

That gives the designer something to work with. The next step might be to adjust the board, make a visual cue clearer, test the level with people, or keep the original idea.

AI is more useful when its output helps the designer ask the right question. Even a difficulty measurement needs context: does it describe the challenge the designer intended, and is that challenge appropriate for the players who will encounter it?

Conclusion

AI can contribute to the evaluation of a Gardenscapes level, but a successful automated test answers only the questions that test was designed to address.

A complete assessment must also consider whether players understand their options, whether the restrictions serve the design, and whether the level fits the point in the game where it appears.

That is why the final judgment needs human input and evidence from real players. A score can start the discussion. It cannot settle whether the experience is clear, satisfying, and worth another attempt.

The computer may give the board a passing grade. The player with one move left may have a few notes. The next round of testing should probably hear them out.